System and method for dynamic SKU generation in distribution management

Abstract

To provide a system and method for automated stock keeping unit (SKU) management.SOLUTION: A system 1100 includes a user interface for receiving diverse catalog files, a catalog transformation module, a Real-Time Data Mesh (RTDM) module, a Master Data Governance (MDG) module, a Global Data Repository (GDR), and a search platform. The catalog transformation module, through iterative learning, transforms catalog files to a standard format and predicts categorization and attribute mapping. The RTDM module is configured to perform real-time data exchange. The MDG module validates the transformed catalogs. The GDR stores validated catalogs. Embodiments can include a dynamic SKU creation module and a global pricing engine for real-time pricing, to improve data accuracy and SKU management, thereby facilitating order processing.SELECTED DRAWING: Figure 11

Description

Technical Field

[0001] Cross - reference to Related Applications This application claims the benefit of U.S. Provisional Patent Application No. 63 / 513,078, filed on July 11, 2023, and U.S. Provisional Patent Application No. 63 / ___,078, filed on July 21, 2023. Each of these applications is hereby incorporated by reference in its entirety.

[0002] The disclosed embodiments relate to aspects of user interface (UI) methods and systems. The global distribution industry to date has faced numerous challenges, including supply chain management, inventory control, stock keeping unit (SKU) management, compliance, and evolving consumer expectations.

[0003] Managing SKUs has presented a very large number of challenges that have thus far hindered operational efficiency, scalability, and customer satisfaction. Existing platforms and systems struggle to cope with these complexities, leading to manual processes, data inconsistencies, and scalability limitations. The IT distribution industry faces the major challenge of dealing with the vast number and variety of SKUs. The processes of creating, categorizing, and pricing these SKUs involve a great deal of manual intervention, leading to errors, inconsistencies, and delays. Such inefficiencies not only consume valuable time and resources but also impede the efficient handling of large volumes of SKUs. Additionally, existing platforms lack streamlined processes and self - service functions, leading to further difficulties in collaborating with vendors and resulting in delays, inaccuracies, and inefficiencies in synchronizing product data across the platform.

[0004] Scalability is another critical challenge for IT distribution platforms, especially when dealing with a wide range of vendors offering diverse products, including long-tail and mid-tail suppliers. Maintaining data integrity and ensuring quality controls in SKU management are top priorities. However, current platforms often fail to achieve this, resulting in internal operational challenges and a substandard customer experience. Inconsistent categorization, attribute mapping errors, and data mismatches hinder customers from finding and comparing products due to inconsistent and inaccurate data across the platform.

[0005] Pricing SKUs add complexity when considering factors such as special pricing, margin rules, and real-time market conditions. Existing platforms lack efficient mechanisms for calculating and updating prices, leading to delays, inaccuracies, and lost revenue opportunities. Manual pricing processes exacerbate the problem, resulting in inefficiencies, errors, and potential revenue losses. Furthermore, the concept of virtual SKUs complicates SKU management in the IT distribution domain. Virtual SKUs represent products that are available for customer viewing but have not yet been fully processed. Transitioning virtual SKUs to real SKUs at the time of order requires integration, real-time updates, and efficient backend processes. However, current systems struggle to handle this dynamic transition smoothly, leading to delays, data inconsistencies, and unsatisfactory customer experiences.

[0006] In addition, legacy systems and outdated architectures present significant operational challenges. Diverse data formats, types, and poor handling of legacy systems adopted by different vendors add complexity and hinder integration. As a result, SKU management platforms struggle to efficiently process, convert, and validate vendor catalogs, leading to delays, errors, and operational inefficiencies. There is a need for a real-time, synchronized solution that can efficiently address SKU generation, pricing, and vendor integration. [Overview of the project]

[0007] The global distribution industry is at a critical juncture, grappling with a range of challenges across multiple areas. These obstacles, both historical and newly emerging, require the development of innovative and effective solutions to drive the sector towards growth and efficiency. Among these numerous difficulties, the most critical lie in the areas of supply chain management, inventory and compliance issues, SKU management, the shift to direct-to-consumer models, and rapidly evolving consumer expectations and behaviors.

[0008] In some embodiments, real-time and synchronous solutions for SKU generation on distribution platforms are provided. Distribution and supply chain management have traditionally been outside the core competencies of distributors, and inefficiencies are exacerbated by the difficulty in dealing with disruptions. Market trends increasingly favor direct-to-consumer models, necessitating a reassessment of existing business strategies to adapt to this dynamic change.

[0009] Inventory management presents another essential problem in distribution. The volatile nature of market demand necessitates flexible distribution and supply chains, and the complexity of distribution models, including cloud services and XaaS (Everything as a Service), adds further complexity to the process.

[0010] Further complicating matters are issues surrounding product localization, fluctuating distribution rights, and global SKU management. Coordinating data from diverse OEMs, each with its own systems and processes, adds complexity, while complying with requirements in different jurisdictions introduces potential inefficiencies and errors.

[0011] Furthermore, evolving consumer behavior and expectations necessitate the creation of user-friendly, efficient, and configurable platforms for technology purchases. Traditional methods of customer interaction are being replaced by the demand for real-time and synchronous interactions, requiring companies to evolve to meet these new customer expectations.

[0012] Despite these challenges, the distribution model offers numerous advantages compared to the direct-to-consumer model. Specialized entities handling logistics and distribution allow manufacturers to focus on their core competencies. Extensive distribution networks reach customers in remote locations, and value-added services enhance the overall customer experience. To realize these benefits and effectively maintain the distribution model, adopting real-time and synchronous SKU generation solutions is essential. Addressing current pitfalls and streamlining processes through cutting-edge technology ensures the sustainability and competitiveness of the distribution model.

[0013] Single pane of glass The Single Pane of Glass (SPoG) disclosed herein can provide a comprehensive solution aimed at addressing these challenges through a real-time and synchronous approach to SKU generation. It provides a holistic, user-friendly, and efficient platform that streamlines the distribution process and improves supply chain visibility and inventory management.

[0014] By incorporating real-time tracking and analytics, SPoG provides a valuable overview of inventory levels and product status, ensuring that supply chain management is addressed efficiently in real time. By integrating multiple communication channels into a single platform, SPoG emulates direct consumer channels into the distribution platform, improving the overall customer experience through synchronous interactions.

[0015] SPoG's advanced forecasting capabilities provide an innovative solution for improved inventory management through real-time predictive analytics. These forecasts highlight demand trends and guide companies in mitigating the risk of stockouts or excess inventory in real time.

[0016] The platform further includes a real-time global distribution database, enabling distributors to stay up-to-date with the latest international regulations in real time. This feature reduces the burden of manual tracking, ensuring compliance and facilitating time-free cross-border transactions.

[0017] SKU management and product localization are streamlined through SPoG's integration of data from various OEMs into a single platform, ensuring data consistency and reducing the potential for real-time errors. The platform's highly configurable and user-friendly interface meets the expectations of a new generation of technology buyers, providing real-time access to technology.

[0018] SPoG's flexible and scalable design ensures it remains a future-proof solution, adapting to changing business needs without significant infrastructure changes, and thus meeting the demand for real-time and synchronous interactions in dynamic distribution environments.

[0019] Real-time data mesh (RTDM) Implementing a Real-Time Data Mesh (RTDM) on the platform provides an innovative solution to address the need for real-time and synchronous SKU generation capabilities in the distribution domain. RTDM offers a distributed data architecture that enables real-time data availability across multiple sources and touchpoints.

[0020] RTDM empowers predictive analytics and provides real-time solutions for efficient inventory control. Forecasts of demand trends help companies manage inventory in sync with market fluctuations, mitigating the risk of real-time excess inventory or stockouts.

[0021] Global distribution and compliance are facilitated through real-time updates provided by RTDM, ensuring distributors maintain up-to-date compliance as well as real-time changes and SKU management. The platform significantly reduces the burden of manual tracking and facilitates delay-free cross-border transactions.

[0022] Integrating RTDM data from various OEMs simplifies SKU management and localization, ensures data consistency and reduces the potential for errors, and even meets the demand for real-time, synchronized transactions.

[0023] By enhancing the customer experience, RTDM aggregates and synchronizes data in an intuitive interface, enabling easy access to technology and real-time transactions, meeting the expectations of technology partners in the consumer-driven generation.

[0024] Advantages of SPoG and RTDM integration By integrating the SPoG UI platform with RTDM, an intensive and holistic approach to the technical challenges encountered in a distribution platform focused on real-time and synchronous SKU generation becomes possible. SPoG leverages the capabilities of RTDM to improve supply chain visibility, streamline inventory management, ensure compliance, simplify SKU management, and deliver an outstanding customer experience.

[0025] The real-time tracking and analysis of RTDM significantly improves SPoG's ability to effectively manage the supply chain and inventory, providing accurate and up-to-date information for informed real-time decision-making.

[0026] The integration of SPoG and RTDM ensures the consistency of synchronous data and reduces errors and delays in SKU management and pricing. A central aggregation platform for managing data from various OEMs simplifies product localization and aligns with real-time market needs. This integration highlights the revolutionary aspect of real-time / synchronous SKU generation in the distribution platform and stands out as a new and innovative solution for the evolving global market.

Brief Description of the Drawings

[0027] [Figure 1] In this embodiment, an embodiment of the operating environment of a distribution platform, called a system, is illustrated. [Figure 2] An embodiment of the operating environment of a distribution platform constructed based on the elements introduced in FIG. 1 is illustrated. [Figure 3] An embodiment of a system for supply chain management is illustrated. [Figure 4] An embodiment of an advanced distribution platform including a system for managing complex distribution networks is depicted. This can be an embodiment of a system that provides a technical distribution platform for optimizing the management and operation of distribution networks. [Figure 5] An RTDM module according to one embodiment is shown. [Figure 6] An illustration shows a SPoG UI according to one embodiment. [Figure 7] This is a flowchart illustrating a method for performing comprehensive supply chain management operations using SPoG UI, according to several embodiments of this disclosure. [Figure 8] This is a flowchart illustrating a method for vendor onboarding using SPoG UI, according to some embodiments of the present disclosure. [Figure 9] This is a flowchart illustrating a method for reseller onboarding using the SPoG UI, according to several embodiments of the present disclosure. [Figure 10] This is a flowchart illustrating a method for customer and end-customer onboarding using the SPoG UI, according to several embodiments of this disclosure. [Figure 11] This diagram illustrates one embodiment of a system for managing SKUs in a distribution network, based on several different configurations. [Figure 12] This is a flowchart illustrating a method for managing SKUs in a distribution network according to some embodiments of the present disclosure. [Figure 13] This is a block diagram of exemplary components of a device according to some embodiments of the present disclosure. [Figures 14A-14Q] This document illustrates various screens and functionalities of the SPoG UI through several embodiments. [Modes for carrying out the invention]

[0028] This embodiment may be implemented in hardware, firmware, software, or any combination thereof. Alternatively, the embodiment may be implemented as instructions stored in a machine-readable medium, which can be read and executed by one or more processors. The machine-readable medium may include any mechanism for storing or transmitting information in a format readable by a machine (e.g., a computing device). For example, the machine-readable medium may include read-only memory (ROM), random-access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, and others. Furthermore, firmware, software, routines, and instructions may be described herein as performing specific actions. However, such descriptions are merely for convenience, and it should be understood that such actions are actually the results obtained by a computing device, processor, controller, or other device executing the firmware, software, routines, instructions, etc.

[0029] It should be understood that the actions shown in the illustrative methods are not exhaustive, and other actions may be performed before, after, or between any of the illustrated actions. In some embodiments of this disclosure, the actions may be performed in a different order and / or in a variable manner.

[0030] Figure 1 shows the operating environment 100 of a distribution platform called System 110 in this embodiment. System 110 operates within the context of an information technology (IT) distribution model and responds to the demands of various stakeholders, including customers 120, end customers 130, vendors 140, resellers 150, and other entities involved in the distribution process. This operating environment encompasses a wide range of characteristics and dynamics that contribute to the success and efficiency of the distribution platform.

[0031] Customers 120 within the operating environment of System 110 represent companies or individuals seeking IT solutions that meet their specific needs. These customers may require a diverse range of IT products, such as hardware components, software applications, network equipment, or cloud-based services. System 110 provides customers with a user-friendly interface, enabling them to browse, search, and select the most suitable IT solutions based on their requirements. Furthermore, customers are empowered to access real-time data and analytics through System 110, enabling them to make informed decisions and optimize their IT infrastructure.

[0032] The end customer 130 is the ultimate beneficiary of the IT solutions provided by System 110. They may include businesses or individuals who use IT products and services to improve their operations, productivity, or daily activities. The end customer relies on System 110 to access a wide range of IT solutions and is assured of access to the latest technologies and innovations in the market. System 110 enables the end customer to track their orders, receive updates on delivery status, and access customer support services, thereby improving their overall experience.

[0033] Vendor 140 plays a crucial role within the operating environment of System 110. These vendors encompass manufacturers, distributors, and suppliers providing a diverse range of IT products and services. System 110 serves as a centralized platform for vendors to showcase their products, manage inventory, and facilitate transactions with customers and resellers. Vendors can leverage System 110 to streamline supply chain operations, manage pricing and promotions, and gain insights into customer preferences and market trends. By integrating with System 110, vendors can expand their reach, access new markets, and improve overall visibility and competitiveness.

[0034] Resellers 150 are intermediaries within the distribution model, bridging the gap between vendors and customers. They play a crucial role in the IT distribution ecosystem by connecting customers with the right IT solutions from various vendors. Resellers may include retailers, value-added resellers (VARs), system integrators, or managed service providers. System 110 enables resellers to access a comprehensive catalog of IT solutions, manage their sales pipelines, and provide value-added services to customers. By leveraging System 110, resellers can improve their relationships with their customers, optimize their product offerings, and increase their revenue streams.

[0035] Within the operating environment of System 110, various dynamics and characteristics exist that contribute to its effectiveness. These dynamics include real-time data exchange, integration with existing enterprise systems, scalability, and flexibility. System 110 ensures that relevant data is exchanged in real time among stakeholders, enabling accurate decision-making and timely action. Integration with existing enterprise systems such as Enterprise Resource Planning (ERP) systems, Customer Relationship Management (CRM) systems, and warehouse management systems enables integrated communication and interoperability, eliminating data silos and providing end-to-end visibility.

[0036] Scalability and flexibility are key characteristics of System 110. It can accommodate the growing demands of IT distribution models, whether it's an expanding customer base, an increase in vendors, or a wide range of IT products and services. System 110 is designed to handle large-scale data processing, storage, and analytics, ensuring it can support the evolving needs of distribution platforms. In addition, System 110 leverages a technology stack including .NET, Java, and other preferred technologies, providing a robust foundation for its operation.

[0037] In summary, the operating environment of System 110 within the IT distribution model encompasses customers 120, end customers 130, vendors 140, resellers 150, and other entities involved in the distribution process. System 110 functions as a centralized platform that facilitates efficient collaboration, communication, and transaction processes among these stakeholders. By leveraging real-time data exchange, integration, scalability, and flexibility, System 110 empowers stakeholders to optimize their operations within the IT distribution ecosystem, enhance customer experience, and drive business success.

[0038] Figure 2 shows the operating environment 200 of the distribution platform built with the elements introduced in Figure 1. Within this operating environment, integration points 210 facilitate integrated data flow and connectivity between various customer systems 220, ancillary systems 230, vendor systems 240, reseller systems 250, and other entities involved in the distribution process. This figure illustrates the interoperability and mechanisms that enable efficient collaboration and data-driven decision-making.

[0039] The operating environment 200 may include system 110 as a distribution platform that acts as a central hub for managing and facilitating the distribution process. System 110 may be configured to function and operate as a bridge between customer systems 220, vendor systems 240, reseller systems 250, and other entities within the ecosystem. It can integrate communication, data exchange, and transaction processes to provide stakeholders with a unified and streamlined experience. Furthermore, the operating environment 200 may include one or more integration points 210 to ensure smooth data flow and connectivity. These integration points may include:

[0040] Customer System Integration: Integration point 210 allows system 110 to connect with customer system 220, enabling efficient data exchange and synchronization. Customer system 220 may include various entities, such as customer system 221, customer system 222, and customer system 223. Integration with customer system 220 empowers customers to access real-time inventory information, pricing details, order tracking, and other relevant data, improving their visibility and decision-making capabilities.

[0041] Ancillary System Integration: The integration point 210 can enable system 110 to connect with ancillary system 230, allowing for efficient data exchange and synchronization. Ancillary system 230 can connect with various entities, such as ancillary system 231, ancillary system 23 2 This may include an ancillary system 233. Integration with the ancillary system 220 empowers customers to access real-time inventory information, pricing details, order tracking, and other relevant data, improving their visibility and decision-making capabilities.

[0042] Vendor System Integration: Integration point 210 facilitates the integrated connection between system 110 and vendor system 240. Vendor system 240 may include entities representing the inventory management system, pricing system, and product catalog adopted by the vendor, such as vendor system 241, vendor system 242, and vendor system 243. Integration with vendor system 240 ensures that the vendor can efficiently update product offerings, manage pricing and promotions, and receive real-time order notifications and fulfillment details.

[0043] Reseller System Integration: Integration point 210 provides functionality for the reseller system 250 to connect to system 110. The reseller system 250 may include entities representing sales systems, customer management systems, and service distribution platforms adopted by the reseller, such as reseller system 251, reseller system 252, and reseller system 253. Integration with the reseller system 250 empowers the reseller to access up-to-date product information, manage customer accounts, track sales performance, and provide value-added services to customers.

[0044] Integration with other entity systems: Integration point 210 further enables connections with other entities involved in the distribution process. These entities may include entities such as entity system 271, entity system 272, and entity system 273. Integration with these systems ensures integrated communication and data exchange, promoting collaboration and efficient distribution processes.

[0045] Furthermore, integration point 210 enables connection with record system 280 for additional data management and integration. Representing record system 280 can represent enterprise resource planning (ERP) systems or customer relationship management (CRM) systems, including both future systems and legacy ERP systems such as SAP, Impulse, META, I-SCALA, and others. The record system can contain one or more storage repositories of critical and legacy business data. This facilitates data exchange and synchronization integration between the distribution platform, system 110, and ERP, enabling real-time updates and ensuring the availability of accurate and up-to-date information. Integration point 210 establishes a connection between record system 280 and the distribution platform, enabling stakeholders to leverage the rich data stored in the ERP for efficient collaboration, data-driven decision-making, and streamlined distribution processes. These systems represent internal systems utilized by customers, vendors, and others.

[0046] The integration point 210 within the operating environment 200 is facilitated through standardized protocols, APIs, and data connectors. These mechanisms ensure compatibility, interoperability, and secure data transfer between the distribution platform and connected systems. System 110 establishes communication channels using industry-standard protocols, such as RESTful API, SOAP, or GraphQL, enabling integrated data exchange.

[0047] In some embodiments, system 110 can incorporate authentication and authorization mechanisms to ensure secure access and data protection. Technologies such as OAuth or JSON Web Token (JWT) can be employed to authenticate users, authorize data access, and maintain the integrity and confidentiality of exchanged information.

[0048] In some embodiments, the integration point 210 and the data flow within the operating environment 200 enable the operation of stakeholders within the connected ecosystem. Data generated at various stages of the distribution process, including customer orders, inventory updates, shipping details, and sales analytics, flows efficiently between customer systems 220, vendor systems 240, reseller systems 250, and other entities. This data exchange facilitates real-time visibility, enables data-driven decision-making, and improves operational efficiency throughout the distribution platform.

[0049] In some embodiments, System 110 leverages advanced technologies such as Typescript, NodeJS, ReactJS, .NET Core, C#, and other preferred technologies to support the integration point 210 and enable integrated communication within the operating environment 200. These technologies provide a robust foundation for System 110, ensuring scalability, flexibility, and efficient data processing capabilities. Furthermore, the integration point 210 can also employ algorithms, data analysis, and machine learning techniques to derive valuable insights, optimize distribution processes, and personalize the customer experience. The integration point 210 and the data flow within the operating environment 200 enable stakeholders to operate within a connected ecosystem. Data generated at various touchpoints, including customer orders, inventory updates, pricing changes, or delivery status, flows efficiently between different entities, systems, and components. The integrated data is processed, harmonized, and delivered in real time to the relevant stakeholders through System 110. This real-time access to accurate and up-to-date information empowers stakeholders to make informed decisions, optimize supply chain operations, and improve the customer experience.

[0050] Some elements of the operating environment depicted in Figure 2 may include conventional, well-known elements that are only briefly described herein. For example, each of the customer systems, such as customer system 220, may include a desktop personal computer, workstation, laptop, PDA, mobile phone, or any Wireless Access Protocol (WAP)-enabled device, or any other computing device that can interface directly or indirectly with the Internet or other network connectivity. Each of the customer systems may typically run an HTTP client such as Microsoft Edge, Google Chrome, Opera, or a WAP-enabled browser for mobile devices, and the customer systems may access, process, and display information, pages, and applications available from the distribution platform over the network.

[0051] Furthermore, each customer system may be equipped with a user interface device, such as a keyboard, mouse, trackball, touchpad, touchscreen, pen, or similar device for interacting with a graphical user interface (GUI) provided by a browser. These user interface devices enable users of the customer system to navigate the GUI, interact with pages, forms, and applications, and access data and applications hosted by the distribution platform.

[0052] The customer system and its components can be configured by an operator using an application that includes a web browser running on a central processing unit such as an Intel Pentium processor or a similar processor. Similarly, the distribution platform (system 110) and its components can be configured by an operator using an application that runs on a central processing unit such as an Intel Pentium processor or a similar processor, and / or a processor system that may include multiple processor units.

[0053] Embodiments of a computer program product include a machine-readable storage medium containing instructions for programming a computer to perform the processes described herein. Computer code for communicating with distribution platforms and customer systems, vendor systems, reseller systems, and systems of other entities, and for operating and configuring to process web pages, applications, and other data, can be downloaded and stored on a hard disk or any other volatile or non-volatile storage medium or device, such as ROM, RAM, floppy disks, optical disks, DVDs, CDs, microdrives, magneto-optical disks, magnetic cards, optical cards, nanosystems, or any suitable medium for storing instructions and data.

[0054] Furthermore, computer code for implementing this embodiment can be transmitted and downloaded from the software source via the Internet or any other conventional network connection using communication media and protocols such as TCP / IP, HTTP, HTTPS, Ethernet, etc. The code can also be transmitted via an extranet, VPN, LAN, or other network and executed on a client system, server, or server system using programming languages ​​such as C, C++, HTML, Java, JavaScript, ActiveX, VBScript, and others.

[0055] This embodiment can be implemented in various programming languages ​​running on a client system, server, or server system, and it will be understood that the choice of language may depend on the specific requirements and environment of the distribution platform.

[0056] This allows the operating environment 200 to connect the distribution platform with one or more integration points 210 and data flows, enabling efficient collaboration and a streamlined distribution process.

[0057] Figure 3 shows System 300 for supply chain management. System 300 is a supply chain management solution designed to address the challenges faced by fragmented supply chain ecosystems in the global distribution industry. System 300 can include several interconnected components and modules that work in harmony to optimize supply chain operations, improve collaboration, and drive business efficiency.

[0058] In some embodiments, SPoG UI305 acts as a centralized user interface, providing stakeholders with a unified view of the entire supply chain. It aggregates information from various sources and presents real-time data, analytics, and functionality tailored to the user's specific role and responsibilities. By providing a customizable and intuitive dashboard-style layout, SPoG UI305 enables users to access relevant information and tools, empowering them to make data-driven decisions and efficiently manage supply chain activities.

[0059] For example, logistics managers can use SPoG UI305 to monitor shipment status, track delivery routes, and view real-time inventory levels across multiple warehouses. This data can be visualized through interactive charts and graphs, such as maps showing the current location of each shipment or bar graphs indicating inventory levels by product category. Having a unified view of the supply chain allows logistics managers to identify bottlenecks, optimize routes, and ensure timely goods delivery.

[0060] In some embodiments, the SPoG UI305 integrates with other modules of the System 300 to facilitate real-time data exchange, synchronized operations, and streamlined workflows. Through API integration, data synchronization mechanisms, and an event-driven architecture, the SPoG UI305 ensures a smooth information flow and enables collaborative decision-making across the supply chain ecosystem.

[0061] For example, when a purchase order is generated in SPoG UI305, system 300 automatically updates inventory levels, triggers a notification to the warehouse management system, and initiates the shipping process. This integration enables efficient order fulfillment, reduces manual errors, and improves overall supply chain visibility.

[0062] In some embodiments, the Real-Time Data Mesh (RTDM) module 310 can be configured to provide an integrated flow of data within the supply chain ecosystem. It collects and harmonizes data from multiple sources, ensuring its real-time availability.

[0063] In a non-limiting example, the RTDM module 310 can collect data from a recording system 280 that can represent a variety of systems, including heterogeneous inventory management systems, point-of-sale terminals, or customer relationship management systems. This data is harmonized by aligning formats, standardizing units of measurement, and matching and adjusting inconsistencies. The harmonized data is then available in real time, enabling stakeholders to access accurate and up-to-date information across the supply chain.

[0064] In some embodiments, the RTDM module 310 can be configured to capture data changes in real time across multiple transaction systems. It employs an advanced Change Data Capture (CDC) mechanism that continuously monitors transaction systems to detect updates or changes. The CDC component is specifically designed to work with a variety of transaction systems, including future and legacy ERP systems, customer relationship management (CRM) systems, and other enterprise-scale systems, ensuring compatibility and flexibility for businesses in diverse environments.

[0065] By providing continuous access to real-time data, stakeholders can make timely decisions and respond quickly to changing market conditions. For example, if the RTDM module 310 detects a sudden surge in demand for a particular product, it can trigger an alert to the production team, allowing them to adjust the manufacturing schedule and prevent stockouts.

[0066] In some embodiments, the RTDM module 310 facilitates data management within supply chain operations. It enables real-time harmonization of data from multiple sources, freeing vendors, resellers, customers, and end customers from the constraints imposed by legacy ERP systems. This enhanced flexibility supports improved efficiency, enhanced customer service, and fostered innovation.

[0067] System 300 may also include an Advanced Analytics and Machine Learning (AAML) module 315. The AAML module 315 can leverage powerful analytical tools and algorithms such as Apache Spark, TensorFlow, or scikit-learn, and extracts valuable insights from collected data. It performs advanced analytics, predictive modeling, anomaly detection, and other machine learning operations.

[0068] For example, the AAML module 315 can analyze sales history data to identify seasonal patterns and predict future demand. It can generate forecasts that help optimize inventory levels, ensure inventory usefulness during peak seasons, and minimize excess inventory costs. By leveraging machine learning algorithms, the AAML module 315 automates repetitive tasks, predicts customer preferences, and optimizes supply chain processes.

[0069] In addition to forecasting demand, the AAML module 315 can provide insights into customer behavior, enabling targeted marketing campaigns and personalized customer experiences. For example, by analyzing customer data, the module can identify cross-selling or upselling opportunities and recommend products relevant to individual customers.

[0070] Furthermore, the AAML module 315 can analyze data from various sources, such as social media feeds, customer reviews, and market trends, to gain a deeper understanding of consumer intentions and preferences. This information can be used to inform product development decisions, identify emerging market trends, and adapt business strategies to meet evolving consumer expectations.

[0071] System 300 can provide integration and interoperability capabilities to connect with existing enterprise systems, such as ERP systems, warehouse management systems, and customer relationship management systems. By establishing connectivity and data flow between these systems, System 300 enables seamless data exchange, process automation, and end-to-end visibility across the supply chain. Integration protocols, APIs, and data connectors facilitate integrated communication and interoperability between different modules and components, creating a holistic and connected supply chain ecosystem.

[0072] The implementation and deployment of System 300 can be tailored to meet specific business needs. In some non-exclusive examples, it can be deployed as a cloud-native solution using containerization technologies such as Docker® and orchestration frameworks such as Kubernetes®. This approach ensures scalability, ease of management, and efficient updates across different environments. The implementation process involves configuring the system to align with specific supply chain requirements, integrating it with existing systems, and customizing modules and components based on business needs and preferences.

[0073] System 300 for supply chain management is a comprehensive and innovative solution that addresses the challenges faced by fragmented supply chain ecosystems. It combines the power of SPoG UI305, RTDM module 310, and AAML module 315 with integration with existing systems. By leveraging a diverse technology stack, scalable architecture, and robust integration capabilities, System 300 delivers end-to-end visibility, data-driven decision-making, and optimized supply chain operations. The examples and options provided herein are non-exclusive and can be customized to meet specific industry requirements to achieve efficiency and success in supply chain management.

[0074] Figure 4 shows one embodiment of an advanced distribution platform including a system 400 for managing a complex distribution network, which can be an embodiment of system 300, providing a technology distribution platform for optimizing the management and operation of the distribution network. System 400 includes several interconnected modules, each performing a specific function and contributing to the overall efficiency of supply chain operations. In some embodiments, these modules may include a SPoG UI 405, a Customer Interaction Module (CIM) 410, an RTDM module 415, an AI module 420, an interface display module 425, a personalization interaction module 430, a document hub 435, a catalog management module 440, a performance and forecast marker display 445, a predictive analytics module 450, a recommendation system module 455, a notification module 460, a self-onboarding module 465, and a communication module 470.

[0075] System 400, as an embodiment of System 300, integrates and aggregates supply chain management by leveraging a wide range of technologies and algorithms. These technologies and algorithms facilitate efficient data processing, personalized interactions, real-time analytics, secure communication, and effective management of documents, catalogs, and performance metrics.

[0076] In some embodiments, SPoG UI405 functions as the central interface within System 400, providing stakeholders with a unified view of the entire distribution network. Frontend technologies such as ReactJS, TypeScript, and Node.js are used to create an interactive and responsive user interface. These technologies enable SPoG UI405 to deliver a user-friendly experience, allowing stakeholders to access relevant information, navigate through different modules, and perform tasks efficiently.

[0077] In some embodiments, the CIM410, or Customer Interaction Module, employs algorithms and technologies such as Oracle® Eloqua®, Adobe® Target, and Okta® to manage customer relationships within the distribution network. These technologies enable the module to securely handle customer data, personalize the customer experience, and provide integrated access control to stakeholders.

[0078] In some embodiments, the RTDM module 415, or Real-Time Data Mesh module, is a critical component of the system 400, ensuring a smooth data flow across the supply chain ecosystem. Technologies such as Apache® Kafka®, Apache® Flink®, and Apache® Pulsar are utilized for data ingestion, processing, and stream management. These technologies enable the RTDM module 415 to handle real-time data streams, process large volumes of data, and ensure low-latency data processing. In addition, the module employs a Change Data Capture (CDC) mechanism to capture real-time data updates from various transaction systems, such as legacy ERP and CRM systems. This capability allows stakeholders to access up-to-date and accurate information and make informed decisions.

[0079] In some embodiments, the AI ​​module 420 within the system 400 leverages advanced analytical and machine learning algorithms, including Apache® Spark, TensorFlow®, and scikit-learn®, to extract valuable insights from data. These algorithms enable the module to automate repetitive tasks, predict demand patterns, optimize inventory levels, and improve overall supply chain efficiency. For example, the AI ​​module 420 can use predictive models to forecast demand, allowing stakeholders to optimize inventory management and minimize stockouts and excess inventory.

[0080] In some embodiments, the interface display module 425 focuses on presenting data and information in a clear and user-friendly manner. It utilizes technologies such as HTML, CSS, and JavaScript frameworks like ReactJS to create an interactive and responsive user interface. These technologies enable stakeholders to visualize data using various data visualization techniques, such as graphs, charts, and tables, allowing for efficient data understanding, comparison, and trend analysis.

[0081] In some embodiments, the Personalized Interaction Module 430 utilizes customer data, trend history, and machine learning algorithms to generate personalized recommendations for products or services. In some non-exclusive examples, it can be implemented using Adobe® Target, Apache® Spark, and TensorFlow® for data analysis, modeling, and targeted recommendation delivery. For example, the module can analyze customer preferences and purchase history to provide personalized product recommendations, improve customer satisfaction, and drive sales.

[0082] In some embodiments, the document hub 435 functions as a centralized repository for storing and managing documents within the system 400. In some non-limiting examples, it can be implemented using SeeBurger® and Elastic Cloud for efficient document management, storage, and retrieval. For example, the document hub 435 can use SeeBurger's document management capabilities to classify and organize documents based on type, such as contracts, invoices, product specifications, and compliance documents, allowing stakeholders to easily access and search for relevant documents as needed.

[0083] In some embodiments, the catalog management module 440 enables the creation, management, and distribution of up-to-date product catalogs. This ensures that stakeholders have access to the latest product information, including specifications, pricing, availability, and promotions. In some non-limiting examples, the module can be implemented using Kentico® and Akamai® to integrate and aggregate catalog updates, content delivery, and caching. For example, the module can leverage Akamai's Content Delivery Network (CDN) to deliver catalog information to stakeholders quickly and efficiently, regardless of their geographical location.

[0084] In some embodiments, the performance and forecast marker display 445 collects, analyzes, and visualizes real-time performance metrics and forecasts related to supply chain operations. Tools such as Splunk® and Datadog® are used to enable effective performance monitoring and provide actionable insights. For example, the module can use Splunk's log analysis capabilities to identify performance bottlenecks in the supply chain, enabling stakeholders to take proactive measures to optimize operations.

[0085] In some embodiments, the predictive analytics module 450 employs machine learning algorithms and predictive models to forecast demand patterns, optimize inventory levels, and improve overall supply chain efficiency. Technologies such as Apache® Spark and TensorFlow® are used for data analysis, modeling, and forecasting. For example, the module can use TensorFlow's deep learning capabilities to analyze sales history data and forecast future demand, enabling stakeholders to optimize inventory levels and minimize costs.

[0086] In some embodiments, the recommendation system module 455 focuses on providing intelligent recommendations to stakeholders within a distribution network. It generates personalized recommendations for products or services based on customer data, trend history, and machine learning algorithms. In some non-exclusive examples, it can be implemented using Adobe® Target and Apache® Spark for data analysis, modeling, and delivery of targeted recommendations. For example, the module can leverage Adobe Target's recommendation engine to analyze customer preferences and behavior, deliver personalized product recommendations across various channels, and improve customer engagement to drive sales.

[0087] In some embodiments, the notification module 460 enables the delivery of real-time notifications to stakeholders regarding critical events, updates, or alerts within the supply chain. In some non-limiting examples, it can be implemented using message queues, event-driven architectures, and integrated notification delivery with Apigee®X and TIBCO®. For example, the module can use TIBCO's messaging infrastructure to send real-time notifications to stakeholders' devices, ensuring timely distribution of relevant information.

[0088] In some embodiments, the self-onboarding module 465 facilitates the onboarding process for new stakeholders entering the distribution network. It provides guided steps, tutorials, or documentation to help users become familiar with the system and its functions. In some non-limiting examples, it can be implemented using technologies such as Okta® and Kentico® to ensure secure user authentication, access control, and self-learning resources. For example, the module can leverage Okta's identification and access management capabilities to securely onboard new stakeholders, grant them appropriate access permissions, and guide them through the system's functionality.

[0089] In some embodiments, the communication module 470 enables integrated and aggregated communication and collaboration within the system 400. It provides stakeholders with channels for interaction, message exchange, document sharing, and project collaboration. In some non-limiting examples, it can be implemented using Apigee® Edge and Adobe® Launch to facilitate secure and efficient communication, document sharing, and version control. For example, the module utilizes the API management capabilities of Apigee Edge to ensure secure and reliable communication among stakeholders and enable effective collaboration.

[0090] This allows System 400 to incorporate a variety of modules that utilize a diverse range of technologies and algorithms to optimize supply chain management. These modules include SPoG UI 405, CIM 410, RTDM module 415, AI module 420, interface display module 425, personalized interaction module 430, document hub 435, catalog management module 440, performance and forecast marker display 445, predictive analytics module 450, recommendation system module 455, notification module 460, self-onboarding module 465, and communication module 470, which work together to provide end-to-end visibility, data-driven decision-making, personalized interaction, real-time analytics, and streamlined communication within the distribution network. By incorporating specific technologies and algorithms, efficient data management, secure communication, personalized experiences, and effective performance monitoring become possible, contributing to improved operational efficiency and success in supply chain management.

[0091] Real-time data mesh Figure 5 illustrates an RTDM module 500 according to one embodiment. The RTDM module 500 can be an embodiment of the RTDM module 310 and may include interconnected components, processes, and subsystems configured to enable real-time data management and analysis.

[0092] In some embodiments, the RTDM module 500 represents an effective data mesh and change capture component within the overall system architecture, as depicted in Figure 5. The module is designed to provide real-time data management and harmonization capabilities, enabling efficient operation within the supply chain management domain.

[0093] The RTDM module 500 may include an integration layer 510 (also known as the “records system”) that integrates with various enterprise systems. These enterprise systems may include ERPs, such as SAP®, Impulse, META, and I-SCALA, as well as other data sources. The integration layer 510 can handle data exchange and synchronization between the RTDM module 500 and these systems. Data feeds are established to retrieve relevant information from the records system, such as sales orders, purchase orders, inventory data, and customer information. These feeds enable real-time data updates, ensuring that the RTDM module operates with the most up-to-date and accurate data.

[0094] The RTDM module 500 may include a data layer 520 configured to process and translate data for search and analysis. The RTDM module 500 generates a data mesh as a cloud-based infrastructure designed to provide scalable and fault-tolerant data storage capabilities. Within the data mesh, multiple purpose-specific data stores (PDSs) are deployed to store specific types of data, such as customer data, product data, or inventory data. Each PDS is optimized for efficient data retrieval based on specific use cases and requirements. The PDSs are configured to store specific types of data, such as customer data, product data, financial data, etc. These PDSs function as a harmonized and standardized data repository, ensuring data consistency and integrity across the system.

[0095] In some embodiments, the RTDM module 500 implements a data replication mechanism for capturing real-time changes from multiple data sources, including transactional systems such as ERP (e.g., SAP®, Impulse, META, I-SCALA). The captured data is then processed and harmonized on the fly and converted into a standardized format suitable for analysis and integration. This process ensures that the data is readily available and up-to-date within the data mesh, facilitating real-time insights and decision-making.

[0096] More specifically, the data layer 520 within the RTDM module 500 can be configured as a robust and flexible foundation for managing and processing data within the supply chain ecosystem. In some embodiments, the data layer 520 may contain a highly scalable and robust data lake, which may be called a data lake 522, along with a set of purpose-specific data stores (PDSs), which may be called PDSs 524.1 through 524.N. These components work in harmony to ensure efficient data management, harmonization, and real-time availability.

[0097] In some embodiments, the data layer 520 includes a data lake 522, a novel storage and processing infrastructure designed to accommodate the ever-increasing volume, diversity, and speed of data generated within the supply chain. Built on a scalable distributed file system, such as Apache® Hadoop® Distributed File System (HDFS) or Amazon® S3, the data lake can provide a unified, scalable platform for storing both structured and unstructured data. By leveraging the adaptability and fault tolerance of cloud-based storage, the data lake 522 can aggregate and regulate the influx of data from diverse sources.

[0098] In conjunction with Data Lake 522, multiple purpose-specific data stores PDS524.1 through 524.N can be employed. Each PDS524 can function as a dedicated repository optimized for storing and retrieving specific types of data related to the supply chain domain. In some non-limiting examples, PDS524.1 might be dedicated to customer data, storing information such as customer profiles, preferences, and transaction history. PDS524.2 might focus on product data, encompassing details such as SKU codes, descriptions, pricing, and inventory levels. These purpose-specific data stores enable efficient data retrieval, analysis, and processing, meeting the diverse needs of supply chain stakeholders.

[0099] To ensure real-time data synchronization, data layer 520 can be configured to employ one or more advanced change data capture (CDC) mechanisms. These CDC mechanisms integrate with transaction systems such as legacy ERPs like SAP®, Impulse, META, and I-SCALA, as well as other enterprise-scale systems. CDC constantly monitors and captures any updates, changes, or new transactions in these systems in real time. By capturing these changes, data layer 520 ensures that the data within data lake 522 and PDS 524 remains up-to-date, providing stakeholders with a real-time perspective on the supply chain ecosystem.

[0100] In some embodiments, the data layer 520 can be implemented using one or more frameworks, such as .NET or Java, to facilitate integration with existing enterprise systems, ensuring broad compatibility with existing systems and providing flexibility for customization and extensibility. For example, the data layer 520 can leverage a Java technology stack, including frameworks such as Spring and Hibernate®, to facilitate integration with record systems that have a diverse population of ERP systems and other enterprise-scale solutions. This can facilitate smooth data exchange, process automation, and end-to-end visibility across the supply chain.

[0101] In some embodiments, to facilitate data processing and analysis, the data layer 520 may include, in some non-limiting examples, one or more distributed computing frameworks, such as Apache® Spark or Apache® Flink. These frameworks can enable parallel processing and distributed computing across large datasets stored in data lakes and PDSs. By leveraging these frameworks, supply chain stakeholders can perform complex analytical tasks, apply machine learning algorithms, and derive valuable insights from the data. For example, the data layer 520 can leverage Apache Spark's machine learning libraries to develop forecasting models for demand forecasting, optimize inventory levels, and identify potential supply chain risks.

[0102] In some embodiments, the data layer 520 can incorporate robust data governance and security measures. Fine-grained access control mechanisms and authentication protocols ensure that only authenticated users can access and modify data within the data lake and PDS. Data encryption techniques protect sensitive supply chain information from unauthorized access, both at rest and in transit. In addition, the data layer 520 can implement data lineage and audit trail mechanisms to enable stakeholders to track the origin and history of data, ensuring data integrity and compliance with regulatory requirements.

[0103] In some embodiments, Data Layer 520 can be deployed in a cloud-native environment by leveraging containerization technologies such as Docker® and orchestration frameworks such as Kubernetes®. This approach ensures scalability, resilience, and efficient resource allocation. For example, Data Layer 520 can be deployed on cloud infrastructure provided by AWS®, Azure®, or Google® Cloud, leveraging their managed services and scalable storage options. This enables efficient resource scaling based on demand, minimizes operational overhead, and provides an adaptable infrastructure for managing supply chain data.

[0104] The RTDM Module 500's Data Layer 520 can integrate Data Lake 522, a highly scalable data lake, along with application-specific PDSs PDS 524.1 through 524.N. By employing a high-performance CDC mechanism, Data Layer 520 ensures efficient data management, harmony, and real-time availability. Integrating diverse technology stacks, such as .NET or Java, with distributed computing frameworks like Apache® Spark, enables powerful data processing, advanced analytics, and machine learning capabilities. Robust data governance and security measures ensure data integrity, confidentiality, and compliance. Its scalable infrastructure and efficient integration with existing systems empower supply chain stakeholders to make data-driven decisions, optimize operations, and drive business success in dynamic and complex supply chain environments.

[0105] The RTDM module 500 may include an AI module 530 configured to implement one or more algorithms and machine learning models and analyze data stored in the data layer 520 to derive meaningful insights. In some non-limiting examples, the AI ​​module 530 can apply predictive analytics, anomaly detection, and optimization algorithms to identify patterns, trends, and potential risks within the supply chain. The AI ​​module 530 can continuously learn from new data inputs and adapt its model to provide accurate and up-to-date insights. The AI ​​module 530 can generate forecasts, recommendations, and alerts and publish such insights to a dedicated data feed.

[0106] The data engine layer 540 may comprise a set of interconnected systems responsible for ingesting, processing, transforming, and integrating specific data. Within the RTDM module 500, these systems include a collection of autonomously operating headless engines 540, each representing a distinct functionality. These engines represent distinct functionalities within the system and may include, for example, one or more recommendation engines, a prospect engine, and a subscription management engine. Non-exclusive examples of these headless engines include engines for subscriptions, solutions / bundles, ITAD (IT Asset Determination), renewal, marketing, special pricing, financing, returns / claims, end-users, order tracking, superchain, search, vendor management, professional services, and ESG (environmental, social, and governance). These headless engines leverage harmonized data stored in the data mesh to deliver specific business logic and services. The engines 540 can leverage harmonized data stored in the data mesh to deliver specific business logic and services. Each engine is designed to be pluggable, allowing for flexibility and future expansion of the module's functionality.

[0107] These systems can be configured to receive data from multiple sources, such as transaction systems, IoT devices, and external data providers. The data ingestion process involves extracting data from these sources and converting it into a standardized format. Data processing algorithms are applied to cleanse, aggregate, and condense the data, preparing it for further analysis and integration.

[0108] Furthermore, a data distribution mechanism 545 can be employed to facilitate integration with and access to the RTDM module 500. The data distribution mechanism 545 can include one or more APIs and be configured to facilitate data distribution from the data mesh and engine to various endpoints, including user interfaces, micro frontends, and external systems.

[0109] The Experience Layer 550 focuses on providing an intuitive and user-friendly interface for interacting with supply chain data. Experience Layer 550 can include data visualization tools, interactive dashboards, and user-centric functionality. Through this layer, users can search and analyze real-time data related to various supply chain metrics, such as inventory levels, sales performance, and customer demand. The User Experience Layer supports personalized data feeds, allowing users to customize views based on their roles and responsibilities and receive relevant updates. Users can regularly receive specific data updates, such as inventory changes, pricing updates, or new SKU notifications, tailored to their preferences and roles.

[0110] This means that, in some embodiments, the RTDM module 500 for supply chain management can include integration with a record system and may include one or more data layers with a data mesh and purpose-specific data stores, an AI component, a data engine layer, and a user experience layer. These components work together to provide users with intuitive access to real-time supply chain data, efficient data processing and analysis, and efficient integration with existing enterprise systems. Technical feeds and search within the module ensure that users can find relevant and up-to-date information and insights, make informed decisions, and optimize supply chain operations. Thus, the RTDM module 500 facilitates supply chain management by providing a scalable, real-time data management solution. Its innovative architecture enables rich integration of heterogeneous data sources, efficient data harmonization, and advanced analytical capabilities. The module offers a clear advantage by replicating and harmonizing data from diverse ERPs while maintaining the ability to keep auditable and repeatable transactions, and enabling a unified view for vendors, resellers, customers, end customers, and other entities within the distribution system, including IT distribution systems.

[0111] Single-pane-of-glass UI Figure 6 shows a SPoG UI according to one embodiment, generally referred to as SPoG UI600. In some embodiments, SPoG UI600 can be an embodiment of SPoG UI305 and represents a comprehensive and intuitive user interface designed to provide stakeholders with a unified, customizable view of the entire supply chain ecosystem. It combines various features and functionalities to enable users to gain a comprehensive understanding of the supply chain and efficiently manage its operations.

[0112] SPoG600 enables high-speed data integration in data-rich environments. In today's data-rich environments, traditional UI design often struggles to present large amounts of information in a way that is easy to understand, efficient, and visually appealing. This challenge intensifies when data changes dynamically and in real time, and needs to be effectively displayed in a single-pane environment that emphasizes clean, whitespace-based design.

[0113] The SPoG UI600 integrates functionality with the RTDM modules 310 / 500 to provide stakeholders with a powerful and user-friendly interface for supply chain management. In some embodiments, the SPoG UI600 may include a Unified View (UV) module 605 for providing a customizable and holistic view of the supply chain, and a Real-Time Data Exchange module 610 for ensuring accurate and up-to-date data synchronization based on the RTDM modules 310 / 500. A Collaborative Decision Module 615 facilitates effective communication and collaboration among diverse populations. The RBAC module 620 can be configured to secure access control. The Customization Module 625, Data Visualization Module 630, and Mobile and Cross-Platform Accessibility Module 635 can be configured to improve user experience, data analysis, and accessibility, respectively. In some embodiments, the aforementioned modules can enable stakeholders to make informed decisions, optimize supply chain operations, and promote business efficiency within the supply chain ecosystem.

[0114] SPoG UI600 can include the UV module 605, which provides stakeholders with a centralized, customizable dashboard-style layout. This module allows users to access real-time data, analytics, and functionality tailored to specific roles and responsibilities within the supply chain ecosystem. The UV module 605 acts as a single entry point for users, providing a holistic and comprehensive view of supply chain operations and empowering data-driven decision-making. The UV module 605 can be configured to efficiently manage real-time data while maintaining a visually clean interface without compromising performance. This innovative approach includes a unique UI structure, responsive data visualization, real-time data handling, an adaptive information architecture, and whitespace optimization.

[0115] UV Module 605 is built around a grid-based layout system and can leverage CSS Grid and Flexbox technologies. This structure provides flexibility for creating fluid layouts with elements that automatically adapt to available space and content. HTML5 and CSS3 serve as the foundational technologies for creating the UI, while JavaScript, particularly React.js, manages the dynamic aspects of the UI.

[0116] The SPoG UI600 integrates the UV module 605 with the real-time data exchange module 610 to facilitate continuous data exchange between the SPoG UI600 and the RTDM module 310, enabling the use of one or more data sources, including one or more ERP, CRM, or other sources. Through this module, stakeholders can access up-to-date, accurate, and harmonized data. Real-time data synchronization ensures that the information presented to the SPoG UI600 reflects the latest outlook and developments across the entire supply chain. This integration enables stakeholders to make informed decisions based on accurate and synchronized data.

[0117] In some embodiments, the collaborative decision module 615 within the SPoG UI 600 facilitates real-time collaboration and communication among stakeholders. This module enables the exchange of information, the initiation of workflows, and the sharing of insights and recommendations. By integrating with the RTDM module 310 / 500, the collaborative decision module 615 ensures that stakeholders can collaborate effectively based on accurate and synchronized data. This promotes overall operational efficiency and collaboration within the supply chain ecosystem.

[0118] To ensure functionality and secure, controlled access to data, SPoG UI600 incorporates a role-based access control (RBAC) module 620. Administrators can define roles, grant permissions, and control user access based on their responsibilities and organizational hierarchy. The RBAC module 620 ensures that only authenticated users can access specific features and information, protecting data privacy, security, and compliance within the supply chain ecosystem.

[0119] In some embodiments, the customization module 625 empowers users to personalize their dashboards and tailor the interface to their preferences and needs. Users can arrange widgets, charts, and data visualizations to prioritize information most relevant to specific roles and tasks. This module enables stakeholders to customize their view of their supply chain operations, providing a user-centric experience that improves productivity and ease of use.

[0120] SPoG UI600 can include the Data Visualization Module 630, which enables stakeholders to analyze and interpret supply chain data through interactive dashboards, charts, graphs, and visual representations. Leveraging advanced visualization techniques, this module presents complex data in a clear and intuitive manner. Users can gain insights into key performance indicators (KPIs), trends, patterns, and anomalies, facilitating data-driven decision-making and strategic planning.

[0121] SPoG UI600 can include a mobile and cross-platform accessibility module 635 to ensure accessibility across multiple devices and platforms. Users can access the interface from desktop computers, laptops, smartphones, and tablets, staying connected and informed while on the go. This module optimizes the user experience for different screen sizes, resolutions, and operating systems, ensuring integrated access to real-time data and functionality across various devices.

[0122] The operations shown in an exemplary manner are not exhaustive, and it should be understood that other operations can be performed similarly before, after, or between any of the illustrated operations. In some embodiments of this disclosure, the operations may be performed in a different order and / or vary.

[0123] Figure 7 is a flowchart of Method 700 for performing comprehensive supply chain management operations using SPoG UI, according to several embodiments of the present disclosure. In some embodiments, Method 700 provides operational steps for streamlining supply chain processes, improving decision-making, and optimizing operations within the supply chain ecosystem. In some embodiments, Method 700 performs real-time data retrieval, visualization, customization, collaboration, access control, and cross-platform accessibility functions through SPoG UI. Based on the disclosure herein, the operations of Method 700 may be performed in a different order and / or variably to suit specific implementation requirements.

[0124] In operation 705, the computing device receives user input through the SPoG UI representing a wide range of requests and commands related to supply chain management. User input encompasses actions such as selecting specific data visualizations, accessing different modules or functions, starting workflows, configuring interfaces, and running data-driven analytics. This interactive input mechanism enables stakeholders to effectively engage with the SPoG UI and gain relevant insights to support the decision-making process.

[0125] In operation 710, the computing device processes user input and interacts with the Real-Time Data Exchange module by leveraging integration capabilities with the RTDM module. This integration ensures efficient data retrieval and synchronization, enabling the computing device to access up-to-date and accurate information from diverse data sources within the supply chain ecosystem. By establishing a connection with the RTDM module and leveraging real-time data exchange, the computing device ensures that the insights presented in the SPoG UI reflect the latest deployments and provide a comprehensive view of supply chain operations.

[0126] In Operation 715, the computing device uses a data visualization module to generate an interactive, visually appealing representation of the retrieved supply chain data. This module leverages advanced visualization techniques to create dynamic dashboards, charts, graphs, and other visual elements that effectively communicate key performance indicators, trends, patterns, anomalies, and correlations within the supply chain ecosystem. Through these visualizations, stakeholders can gain valuable insights, identify critical areas, and assess the overall health of supply chain operations.

[0127] In Operation 720, computing devices enable users to personalize dashboards and tailor the SPoG UI interface to specific preferences and needs. The customization module empowers stakeholders to arrange widgets, charts, data visualizations, and other UI components to prioritize information most relevant to their roles and responsibilities. This flexibility ensures a user-centric experience, allowing stakeholders to focus on critical data points and streamline the decision-making process within the SPoG UI.

[0128] In Operation 725, computing devices facilitate real-time collaboration and communication among stakeholders through a collaborative decision module. This module provides features that enable stakeholders to exchange information, share insights and recommendations, initiate workflows, and participate in discussions within the SPoG UI interface. By integrating with the RTDM module, the collaborative decision module ensures that stakeholders can collaborate effectively based on accurate and synchronized data, advancing a cohesive and rapid supply chain ecosystem.

[0129] In Operation 730, computing devices implement a secure access control mechanism through a role-based access control (RBAC) module integrated into the SPoG UI. This module allows administrators to define roles, grant permissions, and control user access based on their responsibilities and organizational hierarchy. By implementing RBAC, computing devices protect data privacy, ensure confidentiality, and maintain regulatory compliance within the supply chain ecosystem. Authenticated parties can access specific features, functionalities, and information based on the roles assigned to them, minimizing the risk of unauthorized access to or misuse of data.

[0130] In Operation 735, computing devices optimize the SPoG UI for integrated accessibility across multiple devices and platforms through a mobile and cross-platform accessibility module. This module ensures that stakeholders can access the SPoG UI interface from desktop computers, laptops, smartphones, and tablets, enabling them to stay connected, informed, and engaged in supply chain operations while on the go. The interface is optimized to provide a consistent and intuitive user experience across different screen sizes, resolutions, and operating systems, facilitating real-time data access and improving stakeholder productivity.

[0131] In Operation 740, computing devices leverage high-speed data modules in data-rich environments to efficiently handle real-time data while maintaining a visually clear interface. This module incorporates the unique configuration of the SPoG UI structure, responsive data visualization, real-time data handling methods, adaptive information architecture, and optimization techniques. Operation 740 can handle large amounts of dynamic supply chain data in an easily understandable, efficient, and visually appealing way. The SPoG UI's grid-based layout system is powered by CSS® Grid and Flexbox® technologies, allowing UI elements to fluidly adapt to available space and content, while simultaneously enabling HTML5, CSS3, and JavaScript (especially React.js) to manage the dynamic aspects of the interface.

[0132] In summary, Method 700, depicted in Figure 7, outlines a comprehensive approach to supply chain management through the SPoG UI. By leveraging real-time data search, visualization, customization, collaboration, access control, and cross-platform accessibility features, stakeholders gain valuable insights into supply chain operations and can make informed decisions. This method facilitates efficient integration with RTDM modules, ensuring accurate and up-to-date data synchronization. Through personalized dashboards, interactive data visualizations, collaborative decision-making, secure access control, and cross-device accessibility, the SPoG UI empowers stakeholders to optimize supply chain operations, enhance collaboration, and drive efficiency in a dynamic and complex supply chain ecosystem.

[0133] Figure 8 is a flowchart of Method 800 for vendor onboarding using SPoG UI, according to several embodiments of the present disclosure. In some embodiments, Method 800 outlines a streamlined and efficient process that leverages the capabilities of SPoG UI to facilitate vendor onboarding into the supply chain ecosystem. By integrating real-time data, collaborative decision-making, and role-based access control capabilities, SPoG UI enables stakeholders to effectively manage and optimize the vendor onboarding process. Based on the disclosures herein, the operations of Method 800 may be performed in a different order and / or can be varied to suit specific implementation requirements.

[0134] In operation 805, the process begins when a vendor expresses interest in participating in the supply chain ecosystem. The computing device uses the SPoG UI to receive vendor information and related details. This may include company profiles, contact information, product catalogs, certifications, and any other relevant data required for the vendor onboarding process.

[0135] In operation 810, the computing device verifies vendor information using integration capabilities with a real-time data exchange module. By leveraging real-time data synchronization and access to external systems, the computing device ensures that vendor details are accurate and up-to-date. This verification step helps maintain data integrity, minimize errors, and establish a highly reliable foundation for the vendor onboarding process.

[0136] In operation 815, the computing device initiates the vendor onboarding workflow through a collaborative decision-making module. This module enables stakeholders involved in the onboarding process, such as procurement personnel, legal teams, and vendor managers, to collaborate and make informed decisions based on vendor information. The SPoG UI facilitates efficient communication, file sharing, and workflow initiation, allowing stakeholders to collectively assess vendor suitability and efficiently progress through the onboarding steps.

[0137] In Operation 820, computing devices employ a role-based access control (RBAC) module to manage access control and authorization throughout the vendor onboarding process. The RBAC module ensures that stakeholders have access only to the specific information and functionality required for their role. This control mechanism protects sensitive data, maintains privacy, and ensures compliance with regulatory requirements. Authenticated stakeholders can securely review and contribute to the vendor onboarding process, fostering a transparent and compliant environment.

[0138] In Operation 825, computing devices provide stakeholders with a comprehensive view of the vendor onboarding process through the SPoG UI's Unified View (UV) module. This module presents an intuitive, customizable dashboard-style layout that aggregates relevant information, milestones, and tasks associated with the vendor onboarding process. Stakeholders can monitor progress, track documentation requirements, and access real-time updates to ensure the efficient and timely completion of onboarding tasks.

[0139] In Operation 830, computing devices enable stakeholders to interact with the SPoG UI data visualization module, which provides dynamic visualizations and analyses related to the vendor onboarding process. Through interactive charts, graphs, and reports, stakeholders can evaluate key performance indicators, identify bottlenecks, and gain insights into the overall efficiency of the vendor onboarding process. This data-driven approach empowers stakeholders to make informed decisions, allocate resources effectively, and optimize the onboarding workflow.

[0140] In Operation 835, computing devices facilitate integrated collaboration among stakeholders involved in the vendor onboarding process through a collaborative decision module. This module enables real-time communication, document sharing, and workflow coordination, allowing stakeholders to streamline the onboarding process. By providing a centralized platform for discussion, feedback, and approval, the SPoG UI promotes efficient collaboration and reduces latency in the vendor onboarding workflow.

[0141] In Operation 840, computing devices use the SPoG UI's workflow management module to ensure effective management and tracking of the vendor onboarding process. This module allows stakeholders to define and manage the sequence of tasks, approvals, and reviews necessary for successful vendor onboarding. Workflow templates can be configured, enabling standardization and repeatability of the onboarding process. Stakeholders can monitor the status of each task, track its completion, and receive notifications to ensure timely progress.

[0142] In operation 845, the computing device captures and records vendor onboarding activity within the SPoG UI's audit trail module. This module maintains a detailed history of the onboarding process, including actions taken, documents reviewed, and decisions made. The audit trail enhances transparency, accountability, and compliance, providing stakeholders with a highly reliable record for future reference and potential audits.

[0143] In Operation 850, computing devices complete the vendor onboarding process within the SPoG UI. Once all necessary steps, reviews, and approvals are complete, the vendor is formally onboarded into the supply chain ecosystem. The SPoG UI provides stakeholders with a summary of the onboarding process, enabling them to confirm the completion of all requirements and initiate further actions such as signing contracts, listing products, and collaborating.

[0144] In conclusion, Method 800, as depicted in Figure 8, outlines a streamlined and efficient vendor onboarding process using the SPoG UI. By leveraging real-time data integration, collaborative decision-making, role-based access control, comprehensive visualization, and workflow management capabilities, the SPoG UI empowers stakeholders to successfully onboard vendors into the supply chain ecosystem. This process ensures data accuracy, fosters transparency, enhances collaboration, and promotes informed decision-making throughout the vendor onboarding workflow. The SPoG UI's intuitive interface, combined with customizable features and notifications, streamlines the onboarding process, reduces manual work, and optimizes vendor integration within a dynamic and complex supply chain environment.

[0145] Figure 9 is a flowchart of Method 900 for Reseller Onboarding Using SPoG UI, according to several embodiments of the present disclosure. Method 900 outlines a streamlined and efficient process that leverages the capabilities of SPoG UI to facilitate the onboarding of resellers into the supply chain ecosystem. By integrating real-time data, collaborative decision-making, and role-based access control capabilities, SPoG UI enables stakeholders to effectively manage and optimize the reseller onboarding process. Based on the disclosures herein, the operations of Method 900 may be performed in a different order and / or can be varied to suit specific implementation requirements.

[0146] In operation 905, the process begins when a reseller expresses interest in participating in the supply chain ecosystem. The computing device uses the SPoG UI to receive the reseller's information and related details. This includes company profile, contact information, legal entity verification, reseller agreement, and any other relevant data required for the reseller onboarding process.

[0147] In Operation 910, the computing device verifies reseller information using its integration capabilities with a real-time data exchange module. By leveraging real-time data synchronization and access to external systems, the computing device ensures that reseller details are accurate and up-to-date. This verification step helps maintain data integrity, minimizes errors, and establishes a highly reliable foundation for the reseller onboarding process.

[0148] In Operation 915, the computing device initiates the reseller onboarding workflow through a collaborative decision-making module. This module enables stakeholders involved in the onboarding process, such as sales representatives, legal teams, and customer managers, to collaborate and make informed decisions based on the reseller's information. The SPoG UI facilitates integrated communication, file sharing, and workflow initiation, allowing stakeholders to collectively assess the reseller's suitability and efficiently progress through the onboarding steps.

[0149] In Operation 920, computing devices employ a role-based access control (RBAC) module to manage access control and authorization throughout the reseller onboarding process. The RBAC module ensures that stakeholders have access only to the specific information and functionality required for their role. This control mechanism protects sensitive data, maintains privacy, and ensures compliance with regulatory requirements. Authenticated stakeholders can securely review and contribute to the reseller onboarding process, fostering a transparent and compliant environment.

[0150] In Operation 925, computing devices provide stakeholders with a comprehensive view of the reseller onboarding process through the SPoG UI's Unified View (UV) module. This module presents an intuitive, customizable dashboard-style layout that aggregates relevant information, milestones, and tasks associated with the reseller onboarding process. Stakeholders can monitor progress, track documentation requirements, and access real-time updates to ensure the efficient and timely completion of onboarding tasks.

[0151] In Operation 930, computing devices enable stakeholders to interact with the SPoG UI data visualization module, which provides dynamic visualizations and analyses related to the reseller onboarding process. Through interactive charts, graphs, and reports, stakeholders can evaluate key performance indicators, identify bottlenecks, and gain insights into the overall efficiency of the onboarding process. This data-driven approach empowers stakeholders to make informed decisions, allocate resources effectively, and optimize the reseller onboarding workflow.

[0152] In Operation 935, computing devices facilitate efficient collaboration among stakeholders involved in the reseller onboarding process through a collaborative decision module. This module enables real-time communication, document sharing, and workflow coordination, allowing stakeholders to streamline the onboarding process. By providing a centralized platform for discussion, feedback, and approval, the SPoG UI promotes efficient collaboration and reduces delays in the reseller onboarding workflow.

[0153] In Operation 940, the computing device records and maintains an audit trail of reseller onboarding activities within the SPoG UI's audit trail module. This module captures detailed information about actions taken, decisions made, and documents reviewed during the onboarding process. The audit trail enhances transparency, accountability, and compliance, and serves as a valuable reference for future audits, reviews, and evaluations.

[0154] In Operation 945, the computing device completes the reseller onboarding process within the SPoG UI. Once all necessary tasks, reviews, and approvals are complete, the reseller is formally onboarded into the supply chain ecosystem. The SPoG UI provides stakeholders with a summary of the onboarding process, ensuring all requirements are met and facilitating further actions such as contract signing, product listing, and collaboration with the reseller.

[0155] In conclusion, Method 900, as depicted in Figure 9, highlights a streamlined and efficient reseller onboarding process using SPoG UI. By leveraging real-time data integration, collaborative decision-making, role-based access control, comprehensive visualization, and audit trail functionality, SPoG UI empowers stakeholders to successfully onboard resellers into the supply chain ecosystem. SPoG UI's intuitive interface, customizable features, and robust collaboration capabilities streamline the onboarding process, enhance transparency, and facilitate efficient communication among stakeholders. SPoG UI's data visualization capabilities promote data-driven decision-making, while the audit trail ensures compliance and provides a highly reliable record of onboarding activities. Through the effective use of SPoG UI, the reseller onboarding process becomes a well-orchestrated workflow, optimizing reseller integration and promoting business success in a dynamic supply chain environment.

[0156] Figure 10 is a flowchart of Method 1000 for customer and end-customer onboarding using the SPoG UI, according to several embodiments of the present disclosure. Method 1000 outlines a comprehensive, user-centric approach to efficiently onboarding customers and end-customers into the supply chain ecosystem. By leveraging the capabilities of the SPoG UI, including real-time data integration, collaborative decision-making, and personalized user experiences, stakeholders can successfully onboard and engage customers and provide an efficient and adaptive onboarding experience. Based on the disclosures herein, the operations of Method 1000 may be performed in a different order and / or vary to suit specific implementation requirements.

[0157] In operation 1005, the process begins when a potential customer or end customer expresses interest in participating in the supply chain ecosystem. A computing device utilizes the SPoG UI to capture customer or end customer information, preferences, and requirements necessary for the onboarding process. This includes contact details, company profile, industry-specific preferences, and any other relevant data.

[0158] In operation 1010, the computing device verifies customer or end-customer information using real-time data integration capabilities with external systems. By synchronously accessing data from various sources, such as customer relationship management (CRM) systems or other enterprise-scale solutions, the computing device ensures the accuracy and completeness of customer or end-customer details. This verification step helps establish a highly reliable foundation for the onboarding process and improves data integrity.

[0159] In operation 1015, the computing device initiates the customer or end-customer onboarding workflow through a collaborative decision-making module. This module facilitates integrated communication and collaboration among stakeholders involved in the onboarding process, such as sales representatives, account managers, and customer support teams. The SPoG UI provides a centralized platform for stakeholders to collectively assess customer requirements, define personalized onboarding journeys, and make informed decisions throughout the entire onboarding process.

[0160] In Operation 1020, computing devices use a role-based access control (RBAC) module to manage access control and authorization during the onboarding process. The RBAC module ensures that stakeholders have appropriate access to customer or end-customer data based on their roles and responsibilities. This control mechanism protects sensitive data, maintains data privacy, and ensures compliance with regulatory requirements. Authenticated stakeholders can securely review, update, and track the onboarding process, fostering a transparent and compliant onboarding environment.

[0161] In Operation 1025, computing devices leverage the SPoG UI's Unified View (UV) module to provide stakeholders with a comprehensive, customizable dashboard-style layout of the customer or end-customer onboarding process. This module aggregates relevant information, tasks, and milestones associated with the onboarding journey, providing stakeholders with a holistic view of the onboarding process. Stakeholders can monitor status, review documentation, and access real-time updates to ensure an efficient and integrated onboarding experience.

[0162] In operation 1030, computing devices utilize the SPoG UI's data visualization module to provide dynamic visualizations and analytics related to the onboarding process. Through interactive charts, graphs, and reports, stakeholders gain insights into key onboarding metrics, customer engagement levels, and potential bottlenecks. This data-driven approach empowers stakeholders to make informed decisions, optimize their onboarding strategies, and personalize the onboarding experience for each customer or end customer.

[0163] In operation 1035, computing devices enable stakeholders to interact with a collaborative decision module, facilitating integrated collaboration during the onboarding process. Stakeholders can share documents, initiate workflows, and exchange information in real time. The SPoG UI facilitates efficient communication, reduces latency, and ensures coordination among stakeholders involved in customer or end-customer onboarding.

[0164] In operation 1040, computing devices employ customization modules to enable stakeholders to personalize the onboarding experience for each customer or end customer. Stakeholders can tailor interfaces, workflows, and communications to align with customer or end customer preferences, industry-specific requirements, and strategic objectives. Customization capabilities improve customer satisfaction and engagement throughout the onboarding process.

[0165] In operation 1045, computing devices utilize the audit trail module within the SPoG UI to maintain a detailed record of customer or end-customer onboarding activities. This module captures information about actions taken, decisions made, and documents reviewed throughout the onboarding process. The audit trail enhances transparency, accountability, and compliance, and serves as a valuable reference for future audits, reviews, and evaluations.

[0166] In operation 1050, the computing device completes the customer or end-customer onboarding process within the SPoG UI. Once all necessary tasks, reviews, and approvals are complete, the customer or end-customer is formally onboarded into the supply chain ecosystem. The SPoG UI provides stakeholders with a summary of the onboarding process, ensuring all requirements are met and facilitating further actions such as account activation, service delivery, and personalized customer engagement.

[0167] In conclusion, Method 1000, depicted in Figure 10, illustrates the customer and end-customer onboarding process facilitated by SPoG UI. By leveraging real-time data integration, collaborative decision-making, role-based access control, comprehensive visualization, customization, and audit trail functionality, SPoG UI empowers stakeholders to successfully onboard customers and end-customers within the supply chain ecosystem. SPoG UI's intuitive interface, personalized features, and robust collaboration capabilities streamline the onboarding process, enhance transparency, and facilitate efficient communication among stakeholders. SPoG UI's data visualization capabilities promote data-driven decision-making, while customization and audit trail modules ensure a coordinated and compliant onboarding experience. Through the effective use of SPoG UI, the customer and end-customer onboarding process becomes an integrated workflow, optimizing customer and end-customer integration and fostering business success within a dynamic supply chain environment.

[0168] Figure 11 illustrates a system 1100 for automated SKU management in several embodiments. In some embodiments, the system 1100 includes a UI 1105, a data layer 1110, and a catalog conversion module 11 20 By leveraging its functionality, a comprehensive solution for SKU management can be provided. System 1100 can be configured to improve the accuracy and efficiency of the SKU management process by enabling data exchange, real-time SKU creation, and dynamic pricing. System 1100 can also be configured to empower vendors and entities to effectively manage SKUs, optimize inventory levels, and deliver a superior customer experience in a rapidly evolving market.

[0169] System 1100 is provided for the efficient management of a large number of SKUs on a distribution platform. The system aims to automate SKU management and facilitate easy self-service by integrating with vendors. In some embodiments, one or more SKUs can be generated as virtual SKUs until an order is placed, at which point they become actual SKUs. The entire process is designed to be synchronous, ensuring an efficient and integrated experience for customers. By presenting SKUs on the customer platform, the system can handle a vast number of SKUs without burdening the backend system. This approach enables scalability and avoids operational overhead. The SKU creation process utilizes AI / ML algorithms and proprietary algorithms within the distribution platform to transform the catalog and create virtual SKUs. Data integrity and validation are critical aspects addressed by the system, particularly in the Master Data Governance (MDG) module. The MDG module ensures data accuracy, consistency, and quality control. The system also incorporates a pricing engine for fetching and calculating prices for SKUs, including special pricing. The system architecture enables dynamic SKU creation, efficient data processing, and vendor integration, thereby allowing for scalability and aggregation of products from multiple vendors. The virtual SKU concept, powered by AI / ML algorithms and rule engines, represents a significant paradigm shift that overcomes scaling challenges and enables handling a wider range of SKUs. Through self-service capabilities, vendors can upload catalogs without manual intervention, reducing operational overhead. The system incorporates caching mechanisms and data governance practices to manage the SKU lifecycle, identify inactive SKUs, and retain historical data for compliance and security purposes. Overall, the system's innovative approach to SKU management, virtual SKU creation, and pricing optimization drives improvements in scalability, operational efficiency, and customer experience.

[0170] In some embodiments, the system 1100 may include one or more interconnected modules and subsystems, each performing a specific function and contributing to SKU management. In some embodiments, these components may include a catalog transformation module, a real-time data mesh (RTDM) module, a master data governance (MDG) module, a global data repository (GDR), a search platform, a dynamic SKU creation module, and a global pricing engine (GPE). In some embodiments, the real-time data mesh (RTDM) module (reference no. 1110) and the engine layer (reference no. 1140) play a crucial role in facilitating real-time data management and processing.

[0171] In some embodiments, UI1105 can function as the central point of user interaction within system 1100. In non-limiting examples, UI1105 can be an embodiment of SPoG UI, such as SPoG UI305, 410, 600, or any other UI, enabling a user, for example, one or more vendors, to easily navigate through different modules, access relevant information, and perform various tasks related to SKU management. UI1105 is designed to be intuitive, user-friendly, and responsive, enabling the user to interact with the system efficiently.

[0172] In some embodiments, the data layer 1110 can be configured to enable efficient data flow across the SKU management ecosystem. The data layer 1110 can be an embodiment of the RTDM modules 310, 415, 500, or any other data layer, and can encompass a data lake that serves as a scalable and robust storage infrastructure for storing structured and unstructured data related to SKUs. In some embodiments, the data layer 1110 integrates with the RTDM modules to enable real-time data exchange and synchronization. This integration ensures that the data within the data layer 1110 is up-to-date and readily available for SKU management operations.

[0173] In some embodiments, the data layer 1110 may be an embodiment of an RTDM module, such as RTDM module 310 or RTDM module 415, or RTDM module 500. In some embodiments, the data layer 1110 may be a separate data layer that interacts with the RTDM module. As described above, the RTDM module may be configured to function as an ERP-independent real-time data mesh. In some embodiments, the RTDM module collects data from a record system layer, including data from various enterprise systems such as ERP, and incorporates it into a data lake within the data layer 1110.

[0174] Data Layer 1110 functions as a repository for harmonized and standardized data derived from the RTDM module. Within Data Layer 1110, various purpose-specific data stores are deployed for storing specific types of data, such as customer data, product data, and financial data. These purpose-specific data stores optimize data retrieval based on specific use cases and requirements, ensuring efficient SKU management.

[0175] In the dynamic SKU process, when a customer adds a non-transactional product to their cart, data layer 1110 interacts with the RTDM module to facilitate real-time SKU creation. Data layer 1110 provides the dynamic SKU creation module with the necessary information through access to the most up-to-date and accurate data. This module, along with data layer 1110, utilizes data from other relevant data sources to generate SKUs in real time within the ERP system.

[0176] By leveraging the data available within the data layer 1110 and the real-time capabilities of the RTDM module, system 1100 is configured to enable an efficient and accurate SKU creation process aligned with the latest information. This interaction between the data layer 1110 and the RTDM module facilitates an integrated data flow, enabling a dynamic SKU creation process to be effectively executed within the entire SKU management ecosystem.

[0177] In some embodiments, catalog conversion module 11 20 This can be an embodiment of an AAML module, such as AAML module 315 or AI module 420. In some embodiments, catalog conversion is performed. module 11 20 This can be a separate data layer that interacts with AAML or AI modules. In a non-restrictive example, the catalog transformation module 11 20 This can employ advanced AI / ML algorithms leveraging frameworks such as TensorFlow and PyTorch. These algorithms are trained on a massive dataset of existing vendor catalog files categorized based on a comprehensive data governance process. By utilizing deep learning techniques and neural networks, the module gains the ability to accurately propose categorization and attribute mappings for new catalogs with an accuracy of over 80%.

[0178] In some embodiments, the catalog conversion module 11 20 It operates by processing vendor catalog files received from UI1105 of system 1100. It applies a trained AI / ML model that can incorporate classification and clustering algorithms to predict the most suitable category classification and attribute mapping for each item within the catalog.

[0179] To achieve optimal results, catalog conversion module 11 20 This can consider multiple data points, including product descriptions, keywords, and past mapping patterns. By employing natural language processing (NLP) techniques, meaningful information can be analyzed and extracted from text data, enabling accurate categorization and attribute mapping.

[0180] In addition, catalog conversion module 11 20 It can be configured to facilitate the flexibility and adaptability of the catalog mapping process. It can provide a feedback mechanism that allows vendor users or internal collaborators to review and refine the proposed category classifications and attribute mappings. This feedback can be incorporated into the module's iterative learning process to further improve accuracy and performance over time.

[0181] In a non-limiting example, consider vendor catalogs that include electronic and / or IT products such as laptops, servers, and other computing devices. Catalog Conversion 11 20 The module can analyze product descriptions to identify relevant keywords, apply clustering algorithms to group similar products together, assign appropriate categories, and map relevant attributes such as brand, model, specifications, and pricing to each item.

[0182] In some embodiments, the catalog conversion module module 11 20 It collaborates with the Master Data Governance (MDG) module 1120 to ensure data integrity and validation. Catalog Conversion Module 11 20 It can be configured to communicate with the MDG module, verify the converted catalog, and identify errors or inconsistencies in the categorization and attribute mapping process. Flags or other displays or notifications are generated to alert vendors, administrators, etc., to these issues, enabling them to make necessary corrections and updates.

[0183] Catalog conversion module 11 20 It can operate within the broader System 1100 architecture and be configured to interact with other components, such as the Real-Time Data Mesh (RTDM) module (reference number 1110). Through this interaction, the converted catalog is synchronized in real time, ensuring the availability of accurate and up-to-date product information throughout the entire system.

[0184] Catalog conversion module 11 20 This module leverages advanced AI / ML algorithms and a comprehensive technology stack to convert vendor catalog files into a standardized format. Through the application of deep learning techniques and iterative learning processes, it accurately predicts the category classification and attribute mapping of each catalog item. This module improves the efficiency of SKU management, streamlines operations, and provides enterprises with the capabilities to effectively manage their product catalogs.

[0185] The AI / ML module (AAML) 1115 leverages advanced analytical and machine learning algorithms to improve SKU management capabilities within system 1100. In some non-limiting examples, AAML 1115 can utilize technologies such as Apache Spark, TensorFlow, and scikit-learn to extract valuable insights from data. These algorithms enable AAML 1115 to automate repetitive tasks, predict demand patterns, optimize inventory levels, and improve overall SKU management efficiency.

[0186] In a non-limiting example, the catalog transformation module within system 1100 utilizes data layer 1110 and AAML1115 to transform diverse catalog files into a standardized format. AAML1115, trained on existing catalog datasets, predicts categorical classification and attribute mapping for new catalogs, ensuring accurate and efficient transformation. The transformed catalogs are validated by the MDG module and stored in the GDR, guaranteeing data integrity and consistency.

[0187] The RTDM module, integrated with Data Layer 1110, enables real-time data synchronization across the SKU management ecosystem. This facilitates data exchange and ensures that all system components have access to the most up-to-date information on SKU management operations. This real-time data exchange capability improves the accuracy and efficiency of the SKU management process.

[0188] The dynamic SKU creation module collaborates with the GDR and RTDM modules to enable real-time creation of SKUs for non-transactional products. When a customer adds these products to their cart, the dynamic SKU creation module generates the SKU within the ERP system, facilitating order processing and fulfillment. GPE integrates with data layer 1110 to determine real-time pricing for catalog items based on vendor pricing files, market trends, and pricing history data.

[0189] In some embodiments, SPoG UI1105 provides a user-friendly experience by presenting a unified view of various system elements and functionalities and consolidating multiple system components onto a single platform. It supports multiple data inputs, including vendor catalogs in various formats. In some embodiments, SPoG UI1105 receives this catalog data and interfaces with other system components to enable efficient SKU management.

[0190] In some embodiments, the RTDM module 1110 is a critical component that facilitates real-time data synchronization across system components. It works in conjunction with the SPoG UI 1105 to ensure that the interface displays accurate real-time data, which is important during SKU creation and inventory management. In some embodiments, the RTDM 1110 enables real-time interaction between the user and the system, allowing for immediate reflection of updates across the system.

[0191] By incorporating the AAML module 1115, the system uses a machine learning model to convert catalog files into a standardized format. This module predicts and proposes categorization and attribute mapping of catalog items, maintaining a feedback loop to improve prediction accuracy over time. AAML1115 is trained on existing datasets and uses iterative learning to improve its predictions, contributing to robustness and versatility in handling diverse catalog formats.

[0192] MDG Module Ru is The converted catalog is validated to ensure the accuracy and reliability of the data. In some embodiments, MD G isThe vendor is informed of the error and, via SPoG UI1105, is allowed to re-upload the corrected and revised catalog data. This continuous feedback loop further enriches the data input to AAML module 1115, improving the overall system performance and accuracy.

[0193] The GDR1125 plays a crucial role in the storage of validated catalogs and maintaining data integrity. Working in conjunction with the RTDM1110, it supports real-time data synchronization, ensuring all components have access to the most accurate and up-to-date data. Furthermore, the GDR1125 maintains records of previously validated catalogs, thereby contributing to data integrity. G To facilitate and thereby provide a comprehensive repository for the system.

[0194] The search platform 1130 is integrated with the search and indexing of stored catalogs. It works synergistically with the SPoG UI 1105 to provide users with a product discovery experience. The search platform 1130 also interacts with the RTDM 1110 for real-time data updates, improving the relevance and accuracy of search results.

[0195] In some embodiments, the system 1100 may include additional modules, such as a dynamic SKU creation module 1135 and a global pricing engine (GPE) 1140. The dynamic SKU creation module 1135 dynamically generates SKUs for non-transactional products during checkout, enabling these products to be added to the customer's cart. Meanwhile, the GPE 1140 determines real-time pricing for catalog items based on a variety of factors, including vendor pricing files, market trends, and pricing history data.

[0196] As described above, the data layer 1110 can be an embodiment of an RTDM module (e.g., 310, 415, 500) that leverages the principles of a data mesh to integrate interconnected components, processes, and subsystems, enabling efficient real-time data management and analysis. In a non-limiting example, the RTDM module 1110 can incorporate a cloud-based infrastructure including a data lake (e.g., data lake 522 as depicted in Figure 5) and a purpose-specific data store (PDS) (e.g., PDS 524.1 to 524.N). The data lake can be configured to provide scalable and fault-tolerant data storage capabilities, while the PDS is a repository optimized for storing specific types of data related to an SKU management domain.

[0197] The engine layer 1140 within system 1100 represents a collection of interconnected systems responsible for capturing, processing, transforming, and integrating special data. The engine layer 1140 can be one embodiment of one or more engines 540, as depicted in Figure 5. For example, the engine layer 1140 may include one or more engines configured to operate autonomously within the system and perform individual functionalities, such as an engine configured to determine real-time pricing for one or more catalog items based on various factors, including vendor price files, market trends, and pricing history data.

[0198] In a non-exclusive example, engine layer 1140 could include a recommendation engine, a prospect engine, a subscription management engine, and various other specialized engines tailored to meet specific needs for SKU management. These engines leverage harmonized data stored in the data mesh to deliver targeted business logic and services.

[0199] The engine layer 1140 is designed to receive data from multiple sources, including transaction systems, IoT devices, and external data providers. It processes the incoming data, applies algorithms for data cleansing, summarization, and enrichment, and prepares the data for further analysis and integration within the system.

[0200] In some embodiments, the engine layer 1140 further incorporates a data distribution mechanism (reference number 1145) similar to the data distribution mechanism 545 in Figure 5. This mechanism includes one or more APIs that facilitate the distribution of data from the data mesh and engine to various endpoints, including user interfaces and external systems.

[0201] The engine layer 1140 is an embodiment of the headless engine 540 and operates to facilitate the SKU management system, contributing functionality and advanced algorithms for category assignment, pricing, and other tasks related to SKU management. The engine layer 1140 can be operably connected to other engines and interconnected elements of the RTDM module 500, which operate autonomously to provide specialized services and enable efficient processing of incoming catalog items.

[0202] For example, the engine layer 1140 may include a category assignment engine (CAE) responsible for assigning categories to catalog items based on their attributes and characteristics. In some embodiments, the CAE may be configured to perform accurate and automated category classification by employing one or more techniques, including natural language processing (NLP), machine learning, and rule-based systems.

[0203] Using NLP algorithms, CAE analyzes the text descriptions of catalog items and extracts relevant keywords and phrases. It can then map terms to a predefined category hierarchy, taking into account synonyms, abbreviations, and variations in language usage. Machine learning models trained on vast amounts of historical data recognize patterns and relationships between attributes and categories, helping to improve the accuracy of category classification.

[0204] In addition, CAE utilizes rule-based systems to incorporate specific business rules and logic. These rules can be customized to the unique requirements of the SKU management system, enabling fine-grained control over category assignments. For example, rules can be defined to prioritize certain attributes or consider specific criteria when assigning categories, ensuring consistent and accurate results.

[0205] To support the pricing mechanisms of the SKU management system, the engine layer 1140 incorporates a pricing engine (PE) that determines the optimal pricing for catalog items based on various factors. The PE considers inputs such as vendor price files, market trends, pricing history data, and predefined pricing algorithms or rules.

[0206] PE leverages advanced algorithms to analyze pricing inputs and generate competitive and profitable prices. For example, machine learning algorithms such as regression models or neural networks can be employed to predict optimal price ranges based on sales history data and market dynamics. Alternatively, rule-based systems incorporating pricing strategies and guidelines set by the business can be used.

[0207] Furthermore, the engine layer 1140 interfaces with a Real-Time Data Mesh (RTDM) module, a scalable and fault-tolerant data storage infrastructure that ensures real-time data management and analysis. The RTDM module integrates with various enterprise systems such as ERP, capturing real-time changes and harmonizing the data for efficient processing within the SKU management system.

[0208] By leveraging harmonized and standardized data within the RTDM module, the engine layer 1140 engine makes informed decisions during the category assignment and pricing processes. It retrieves relevant information such as customer data, product data, and financial data from the RTDM module to improve accuracy and consistency.

[0209] Furthermore, the engine layer 1140 incorporates advanced data processing capabilities through technologies such as Apache Spark or Apache Flink. These distributed computing frameworks enable parallel processing and distributed computing across large datasets, facilitating efficient analysis and computation for category allocation and pricing.

[0210] In some embodiments, the engine layer 1140 may also utilize machine learning algorithms, such as clustering or classification models, to identify patterns or group similar catalog items. This can assist in accurate category assignment and facilitate dynamic pricing strategies based on product similarity or customer segmentation.

[0211] Furthermore, Engine Layer 1140 incorporates granular access control mechanisms and authentication protocols to ensure data security and prevent unauthorized access to sensitive information. Data lineage and audit trail mechanisms track the origin and history of data, ensuring compliance with regulatory requirements and maintaining data integrity.

[0212] Engine layer 1140 operates in real time, continuously monitoring incoming catalog items and updating category assignments and pricing information as new data becomes available. It efficiently handles a large volume of catalog items and ensures scalability and performance within the SKU management system.

[0213] Engine Layer 1140, integrated with the RTDM module, leverages advanced algorithms and incorporates customizable business rules to ensure accurate category classification and competitive pricing, ultimately driving productivity and customer satisfaction. By leveraging the capabilities of Engine Layer 1140, System 1100 enables vendors and other entities to automate and streamline category assignment and pricing operations, reducing manual effort and improving overall efficiency. Integration with the RTDM module, advanced algorithms, and data processing technologies empowers Engine Layer 1140's headless engine to deliver accurate and timely results, improving the effectiveness of the SKU management system within System 1100.

[0214] The combination of the RTDM module 1110, the engine layer 1140, and other system components within System 1100 enables efficient management of data flow, real-time visibility, and SKU-related information. Through the integration of diverse technology stacks, data processing frameworks, and distribution mechanisms, enterprises can leverage the power of System 1100 to streamline SKU management processes, optimize operations, and make data-driven decisions.

[0215] Overall, the RTDM module 1110 and engine layer 1140 within system 1100 provide the necessary infrastructure and functionality to handle real-time data management, processing, and analysis, ensuring efficient SKU management operation.

[0216] Figure 11 illustrates one embodiment of an SKU management system called System 1100. In this implementation, System 1100 incorporates specific components that enable efficient and accurate SKU management, including a User Interface (UI) 1105, a Data Layer 1110, and an AI / ML Module (AAML) 1115. These components work together to streamline the processes of SKU creation, management, and pricing, improving the overall efficiency of the system. 。

[0217] Figure 12 illustrates the process flow for SKU generation, which is an integral part of the onboarding process within System 1200, as described in Figure 11. This process flow outlines the steps involved in efficiently generating SKUs based on vendor catalog files, ensuring accuracy, real-time synchronization, and integration within the SKU management ecosystem.

[0218] Operation 1205 includes initiating the SKU generation process when the system receives vendor catalog files. These catalog files contain information about the products offered by the vendor, including descriptions, attributes, pricing details, etc. Triggering the SKU generation process allows the system to convert these catalogs into standardized structured data, facilitating efficient SKU management.

[0219] Operation 1210 may include a catalog transformation step. Within this step, advanced AI / ML algorithms are applied to vendor catalog files, transforming them into a standardized format. Leveraging techniques such as deep learning and neural networks, the algorithms predict the classification and attribute mapping of each item in the catalog. These predictions are based on the analysis of product descriptions, keywords, historical mapping patterns, and other relevant data points. The catalog transformation module further improves the accuracy of predictions by extracting meaningful information from text data using natural language processing (NLP) techniques. Furthermore, this module allows for feedback and refinement of the proposed categorical classification and attribute mapping, incorporating an iterative learning process to continuously improve prediction accuracy.

[0220] To ensure real-time availability and synchronization of SKU information, operation 1215 may include real-time data synchronization. The converted catalog from the catalog conversion step is synchronized with the system's internal data layer. This synchronization ensures that SKU information is kept up-to-date and readily available for SKU management operations. By integrating a Real-Time Data Mesh (RTDM) module, the system facilitates data exchange and synchronization, enabling stakeholders to access the most accurate and up-to-date SKU information.

[0221] motion Work 1220 may include a Master Data Governance (MDG) step, which is crucial for maintaining data integrity and validation. The converted catalog is validated within the MDG module to ensure its accuracy, consistency, and compliance with data governance practices. This validation process identifies errors or inconsistencies in the catalog's categorization and attribute mapping. Stakeholders, such as vendors or administrators, are notified of these issues through instructions or notifications, enabling them to make necessary corrections and updates. The MDG module plays a vital role in maintaining data quality and reliability throughout the entire SKU generation process.

[0222] motion Design 1225 may include data storage and management. Verified and transformed catalogs are stored within the Global Data Repository (GDR) to ensure data integrity and accessibility. The GDR serves as a central repository for standardized and harmonized data derived from the Real-Time Data Mesh (RTDM) module. This data storage mechanism supports real-time data synchronization and promotes the availability of accurate and up-to-date SKU information across the system. In addition, the GDR retains historical data for compliance and security, providing a comprehensive repository of SKU-related information.

[0223] motion Operation 1230 may include a dynamic SKU creation step. During this step, the system generates SKUs for non-transactional products in real time, typically during the checkout process. By leveraging converted and synchronized catalog data, the system dynamically creates SKUs within the ERP system. This enables efficient order processing and fulfillment by associating the appropriate SKU with the selected product. The real-time nature of this process ensures that customers can add non-transactional products to their cart and complete their purchase without delay.

[0224] motionStep 1235 may include pricing decisions. Within this step, the system determines real-time pricing for catalog items. The pricing engine integrates with the data layer and fetches relevant data such as vendor price files, market trends, and pricing history data. Using advanced algorithms such as machine learning models or rule-based systems, the pricing engine analyzes this data to generate competitive and profitable prices for catalog items. Real-time pricing decisions improve the overall pricing optimization process and ensure accurate and up-to-date pricing information for SKU management operations.

[0225] Operation 1240 may include the termination and verification of the SKU generation process. At this stage, the entire SKU generation process is complete, and the system verifies the generated SKUs and pricing information for accuracy. Any necessary actions, such as order processing or further SKU management operations, can be initiated based on the generated SKUs. This step concludes the SKU generation process, which is designed to improve the efficiency, accuracy, and integration of SKU management within the system.

[0226] The process flow depicted in Figure 12 illustrates the steps involved in the SKU generation process related to the onboarding process within System 1200. By leveraging advanced AI / ML algorithms, real-time data synchronization, and data governance practices, the system ensures the accurate and timely generation of SKUs based on vendor catalog files. This process flow supports integration, scalability, and efficiency when managing a large number of SKUs, ultimately improving the overall SKU management capabilities within the system.

[0227] Figure 13 shows the device SThis is a block diagram of exemplary components. One or more computer systems 1300 may be used, for example, to implement any of the embodiments described herein, as well as combinations and subcombinations thereof. A computer system 1300 may include one or more processors (also called central processing units or CPUs), for example, processor 1304. Processor 1304 may be connected to a communication infrastructure, namely bus 1306.

[0228] The computer system 1300 may also include user input / output devices 1303 such as a monitor, keyboard, and pointing device, which can communicate with the communication infrastructure 1306 through a user input / output interface 1302.

[0229] One or more processors 1304 may be graphics processing units (GPUs). In one embodiment, the GPU may be a processor which is a special electronic circuit designed to process mathematically intensive applications. The GPU may have a parallel structure that is efficient for parallel processing of large data blocks, such as mathematically intensive data common to computer graphics applications, images, videos, etc.

[0230] Furthermore, the computer system 1300 may include main or primary memory 1308, such as random access memory (RAM). The main memory 1308 may include one or more levels of cache. The main memory 1308 may have control logic (i.e., computer software) and / or data stored internally.

[0231] Furthermore, the computer system 1300 may include one or more secondary storage devices or memories 1310. The secondary memory 1310 may include, for example, a hard disk drive 1312 and / or a removable storage device or drive 1314.

[0232] The removable storage drive 1314 may interact with the removable storage unit 1318. The removable storage unit 1318 may include a computer-usable or readable storage device having computer software (control logic) and / or data stored thereon. The removable storage unit 1318 may be a program cartridge and cartridge interface (e.g., those found in video game devices), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and / or any other removable storage unit and associated interface. The removable storage drive 1314 may read from and / or write to the removable storage unit 1318.

[0233] The secondary memory 1310 may include other means, devices, components, mediators, or other approaches to enable computer programs and / or other instructions and / or data to be accessed by the computer system 1300. Such means, devices, components, mediators, or other approaches may include, for example, a removable storage unit 1322 and interface 1320. Examples of the removable storage unit 1322 and interface 1320 may include a program cartridge and cartridge interface (for example, those found in video game devices), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and / or any other removable storage unit and associated interface.

[0234] The computer system 1300 may further include a communication or network interface 1324. The communication interface 1324 may enable the computer system 1300 to communicate with and interact with any combination of external devices, external networks, external entities, etc. (referenced individually and collectively in reference number 1328). For example, the communication interface 1324 may enable the computer system 1300 to communicate with an external or remote device 1328 via a communication path 1326, which may be wired and / or wireless (or a combination thereof) and may include any combination of LAN, WAN, Internet, etc. Control logic and / or data may be transmitted to and from the computer system 1300 via the communication path 1326.

[0235] Furthermore, the computer system 1300 may be, to give some non-limiting examples, a personal digital assistant (PDA), a desktop workstation, a laptop or notebook computer, a netbook, a tablet, a smartphone, a smartwatch or other wearable, an appliance, part of the Internet of Things, and / or an embedded system, or any combination thereof.

[0236] Computer system 1300 may be a client or server accessing or hosting any application and / or data through any delivery paradigm, including but not limited to remote or distributed cloud computing solutions, local or on-premises software ("on-premises" cloud-based solutions), "as a service" models (e.g., Content as a Service (CaaS), Digital Content as a Service (DCaaS), Software as a Service (SaaS), Managed Software as a Service (MSaaS), Platform as a Service (PaaS), Desktop as a Service (DaaS), Framework as a Service (FaaS), Backend as a Service (BaaS), Mobile Backend as a Service (MBaaS), Infrastructure as a Service (IaaS), etc.), and / or hybrid models including any combination of the aforementioned examples or other service or delivery paradigms.

[0237] Any applicable data structures, file formats, and schemas in computer system 1300 may be derived from standards including, but not limited to, JavaScript Object Notation (JSON), Extended Markup Language (XML), Yet Another Markup Language (YAML), Extended Hypertext Markup Language (XHTML), Wireless Markup Language (WML), MessagePack, XML User Interface Language (XUL), or any other functionally similar representations, either alone or in combination. Alternatively, proprietary data structures, formats, or schemas may be used either exclusively or in combination with known or open standards.

[0238] In some embodiments, a tangible non-temporary device or product comprising a tangible non-temporary computer-usable or readable medium having control logic (software) stored thereon may also be referred to herein as a computer program product or program storage device. This includes, but is not limited to, a computer system 1300, main memory 1308, secondary memory 1310, and removable storage units 1318 and 1322, as well as tangible products embodying any combination thereof. When such control logic is executed by one or more data processing devices (such as the computer system 1300), such data processing devices can be operated as described herein.

[0239] Figures 14A through 14Q illustrate various screens and functionalities of the SPoG UI related to vendor onboarding, partner dashboards, customer carts, order summaries, SKU generation, order tracking, shipping tracking, subscription history, and subscription changes. Detailed descriptions of each figure are provided below.

[0240] Figure 14A depicts the vendor onboarding start screen, which represents the first step in the vendor onboarding process. This provides a form or interface where vendors can express their interest in participating in the supply chain ecosystem. Vendors can enter basic information such as company details, contact information, and product catalogs.

[0241] Figure 14B depicts a vendor onboarding guide that displays a step-by-step guide or checklist that the vendor follows during the onboarding process. This outlines the necessary tasks and requirements, ensuring that the vendor has a clear understanding of the onboarding process and can proceed smoothly.

[0242] Figure 14C depicts a vendor onboarding call scheduler that facilitates scheduling calls or meetings between vendors and platform partners or representatives who guide them through the onboarding process. Vendors can select a suitable time slot or request a call, ensuring effective communication and assistance throughout the onboarding process.

[0243] Figure 14D depicts a vendor onboarding task list that presents a comprehensive task list or dashboard outlining the specific steps and actions required for successful vendor onboarding. It provides an overview of pending tasks, completed tasks, and upcoming deadlines, helping vendors track progress and ensuring that each onboarding task is completed in a timely manner.

[0244] Figure 14E depicts the vendor onboarding completion screen, which confirms that the vendor onboarding process has been successfully completed. It may display a congratulatory message indicating that the vendor has now been officially onboarded into the supply chain ecosystem, or a summary of completed tasks.

[0245] Figure 14F depicts a partner dashboard that provides partners or stakeholders with a centralized view of relevant information and metrics related to their partnership with the supply chain ecosystem. It provides an overview of performance metrics, key data points, and actionable insights to facilitate effective collaboration and decision-making.

[0246] Figure 14G depicts a customer product cart, where the customer can add items they wish to purchase. It displays a list of selected products, quantities, prices, and other relevant details. The customer can review and modify the contents of their cart before proceeding to the checkout process.

[0247] Figure 14H depicts a customer subscription cart that allows customers to manage their subscription-based purchases. It displays the selected subscription plan, pricing, and duration. Customers can review and modify subscription details before confirming their selection.

[0248] Figure 14I depicts a customer order summary, which provides a summary of the customer's order, including details such as the products or subscriptions purchased, quantities, pricing, and any discounts or promotions applied. This allows the customer to review their order before confirming the purchase.

[0249] Figure 14J depicts a vendor SKU generation screen for generating stock unit (SKU) codes specific to vendor products. It may include fields or options that allow the vendor to specify product details, attributes, and pricing, and the system automatically generates the corresponding SKU codes.

[0250] Figures 14K and 14L illustrate dashboard order summaries, which display summary information about orders placed within the supply chain ecosystem. These present key order details, such as order number, customer name, product or subscription information, quantity, and order status. The dashboard provides an overview of order activity, enabling stakeholders to efficiently track and manage orders.

[0251] Figure 14M depicts a customer subscription cart that allows customers to add, modify, or delete subscription plans. It can display a list of selected subscriptions, pricing, and renewal dates. Customers can manage their subscriptions and make changes according to their preferences and requests.

[0252] Figure 14N depicts a customer order tracking screen that allows customers to track the status and progress of their orders within the supply chain. It displays real-time updates on order fulfillment, including processing, packaging, and shipping. Customers can monitor the movement of their orders and predict delivery times.

[0253] Figure 14O illustrates customer shipment tracking, which provides customers with real-time tracking information about their shipments. This may include details such as carrier, tracking number, current location, and estimated delivery date. Customers can always access information about the whereabouts of their shipments.

[0254] Figure 14P depicts a customer subscription history, which presents a historical record of a customer's subscription activity. It displays a list of previous subscriptions, including the subscription plan, duration, and status. Customers can review their subscription history, track past payments, and view details of previous subscriptions.

[0255] Figure 14Q depicts a customer subscription change dialog, which allows customers to modify their existing subscriptions. It provides options to upgrade or downgrade subscription plans, change billing details, or adjust other subscription-related preferences. Customers can manage their subscriptions according to their evolving needs or preferences.

[0256] The UI screens depicted are not limited. In some embodiments, the UI screens in Figures 14A to 14Q collectively represent the diverse functionality and features provided by the SPoG UI, offering stakeholders a comprehensive and user-friendly interface for vendor onboarding, partnership management, customer interaction, order management, subscription management, and tracking within the supply chain ecosystem.

[0257] One or more computer systems can be configured to perform specific operations or actions by having software, firmware, hardware, or a combination thereof installed on the system that causes the system to perform actions when it is running. One or more computer programs can be configured to perform specific operations or actions by including instructions that, when executed by a data processing device, cause that device to perform actions.

[0258] In one general embodiment, a computer implementation may include integrating multiple communication channels (i.e., touchpoints) between user populations into a unified interactive interface within a computer system, which is referred to herein as the SPoG UI, where SPoG is a central interface component configured to aggregate user interactions, data, and / or functionality from user populations, and the SPoG UI is positioned to facilitate operations across a supply chain ecosystem. The computer implementation may further include using the SPoG UI to manage the end-to-end lifecycle of user interactions. The method may further include collecting data from the user interactions within the SPoG UI. In addition, the method may include analyzing the collected data to generate one or more insights into business growth. The method may further include running one or more artificial intelligence and / or machine learning algorithms to improve business operations based on the analyzed data. The method may further include incorporating periodic updates and improvements to the SPoG UI based on the analyzed data. The method may further include groups comprising distributors, resellers, customers, end customers, vendors, and suppliers, if the user population may include users selected from two or more diverse groups. Other embodiments of this model include corresponding computer systems, devices, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0259] An implementation may include one or more of the following features: Integration may include establishing communication links with multiple existing business platforms; aggregated interaction points may include one or more websites, customer relationship management systems, vendor platforms, and supply chain management systems; managing an end-to-end lifecycle may include one or more initial contact, service performance, and follow-up interactions; collecting data may include monitoring and / or logging user activity within the SPoG UI; analyzing collected data may be performed using advanced statistical algorithms; artificial intelligence and machine learning algorithms may include predictive analytics to identify market trends; artificial intelligence and machine learning algorithms may include recommendation systems to personalize user interactions; improvements may be based on analytics received through the SPoG UI and / or analyzed user feedback. Implementations of the described techniques may include hardware, methods or processes, or computer media.

[0260] In one common embodiment, the system may include a communications integration module configured to integrate multiple communication channels (i.e., touchpoints). The system may further include an aggregation module configured to combine the integrated communication channels into a unified interactive interface, which is referred to herein as the SPoG UI, where the SPoG is a central interface component configured to aggregate user interactions, data, and / or functionality from a user population, and the SPoG UI is positioned to facilitate operations across the supply chain ecosystem. The system may further include a lifecycle management module configured to manage the end-to-end lifecycle of user interactions within the SPoG. The system may additionally or alternatively include a data collection module configured to automatically collect data from user interactions within the SPoG. The system may further include a data analysis module configured to generate one or more insights based on the collected data. The system may further include an artificial intelligence module configured to execute one or more AI and / or ML algorithms based on the analyzed data. The system may further include groups of distributors, resellers, customers, end customers, vendors, and suppliers, if the user population may include users selected from two or more diverse groups. Other embodiments of this model include corresponding computer systems, devices, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of this method.

[0261] A system and method for automating SKU management may include a user interface configured to receive catalog files in two or more diverse formats from a user; a catalog transformation module configured to convert catalog files into a standard format and predict the categorization and attribute mapping of at least one catalog item, wherein the catalog transformation module proposes the categorization and attribute mapping using iterative learning; a real-time data mesh (RTDM) module for ensuring real-time data exchange and synchronization between system components, wherein the RTDM module facilitates real-time interaction between the user and the system; a master data governance (MDG) module for validating the transformed catalog and communicating any errors back to the vendor; and a global data repository (GDR) for storing the validated catalog and maintaining the data integrity of the stored catalog.

[0262] The system and method may include a search platform for indexing and searching stored catalogs. The user interface is a single-pane-of-glass user interface (SPoG UI). A dynamic SKU creation module for generating SKUs for catalog items in an ERP system, where a catalog item represents one or more non-transactional products, and generating an SKU allows one or more products to be added to a customer's cart. GDR is integrated with the RTDM module to support real-time data synchronization across the system. The MDG module generates a display showing one or more errors that occurred in the catalog conversion process accompanying one or more converted catalogs. A global pricing engine (GPE) for determining real-time pricing for catalog items. The GPE determines product pricing based on one or more of the following: vendor pricing files, market trends, and pricing history data. The implementation may include one or more of the following features: a method in which interactive elements include links to various business platforms; a method in which user interactions include actions, which may include one or more clicks, hovers, and input data; a method in which collected data is analyzed using advanced statistical algorithms; and a method in which personalized content is generated based on recommendation algorithms. The method may further include updating the SPoG UI based on user feedback and data analysis results. Implementations of the described method may include hardware, methods or processes, or computer media.

[0263] It should be understood that the detailed description, rather than the abstract, is intended to be used to interpret the claims. The abstract may describe one or more embodiments of the invention intended by the inventors, but not all of which are exemplary, and is therefore not intended to limit the invention and the appended claims in any way.

[0264] The present invention has been described above with the assistance of function-building blocks illustrating the implementation of specific functions and their relationships. For convenience of explanation, the boundaries of these function-building blocks are arbitrarily defined herein. Alternative boundaries can be defined, as long as the specific functions and their relationships are adequately performed.

[0265] The prior description relating to specific embodiments fully illustrates the general nature of the invention, and such specific embodiments can be readily modified and / or applied to various uses without departing from the general concept of the invention, without requiring any unnecessary experimentation, by applying the knowledge of those skilled in the art. Such applications and modifications are therefore intended to be within the meaning and scope of the equivalent embodiments disclosed, based on the teachings and guidance presented herein. The expressions or terms herein are for illustrative purposes only, not limitation, and therefore should be understood to those skilled in the art to be interpreted in light of the teachings and guidance.

[0266] The breadth and scope of the present invention should not be limited by any of the exemplary embodiments described above, but should be defined solely by the following claims and their equivalents.

Claims

1. A system for automating SKU management, wherein the system A user interface configured to receive catalog files in two or more different formats from the user, A catalog conversion module is configured to apply an AI / ML algorithm to the catalog file, convert the catalog file into a standardized format, predict category classification and attribute mapping for at least one catalog item in the catalog file, incorporate vendor reviews of category classification and attribute mapping to provide feedback and refine the category classification and attribute mapping, and incorporate that feedback into iterative learning. A real-time data mesh (RTDM) module comprising an integration layer, a data layer, an AI module, a data engine layer, and an experience layer, wherein the RTDM module synchronizes the catalog files converted by the catalog conversion module with the data layer in order to ensure data exchange and synchronization across the components of the system. A master data governance (MDG) module is configured to perform at least one of the following using master data: schema validation, detection of identical SKUs, and attribute consistency checks on the catalog file converted by the catalog conversion module, and to identify one or more structural errors, classification errors, or pricing inconsistencies in the category classification and attribute mapping predictions made by the catalog conversion module. A global data repository (GDR) is configured to facilitate the operation of the master data governance module by storing catalog files verified by the master data governance module and maintaining the data integrity of the stored catalog files. A system in which the search platform is configured to index catalog files stored in the global data repository in order to facilitate real-time searching of SKU records for optimal product discovery and classification.

2. The system according to claim 1, wherein the user interface is a single-pane-of-glass user interface (SPoG UI).

3. The system according to claim 1, wherein the MDG module generates an indication of one or more errors that occurred in a catalog conversion process associated with one or more converted catalogs.

4. A computerized method for automating SKU management, wherein the method is The user interface allows for receiving catalog files in two or more diverse formats from the user, Using a catalog conversion module, the catalog file is converted to a standardized format, and the categorization and attribute mapping are continuously incorporated through vendor review of categorization and attribute mapping, thereby providing feedback and refining the categorization and attribute mapping, incorporating that feedback into iterative learning, and predicting the categorization and attribute mapping for at least one catalog item in the catalog file. A real-time data mesh (RTDM) module, including an integration layer, a data layer, an AI module, a data engine layer, and an experience layer, is used to ensure data exchange and synchronization between system components by synchronizing the catalog files converted by the catalog conversion module with the data layer. Using the Master Data Governance (MDG) module, at least one of the following is performed on the catalog file converted by the Catalog Conversion Module using the master data: schema validation, detection of identical SKUs, and attribute consistency checks; and one or more structural errors, classification errors, or pricing inconsistencies in the category classification and attribute mapping predictions made by the Catalog Conversion Module. By using the Global Data Repository (GDR) to store the catalog files verified by the master data governance module and to maintain the data integrity of the stored catalog files, the operation of the master data governance module is facilitated. To facilitate real-time searching of SKU records for optimal product discovery and classification using a search platform, the catalog files stored in the global data repository are indexed, Using a dynamic SKU creation module, when one or more non-transactional products are added to a customer's cart, an SKU for the non-transactional product is generated within the ERP system. A computerized method comprising using a global pricing engine (GPE) to determine real-time pricing for the catalog items, wherein the price is determined based on one or more of vendor price files, market trends, and pricing history data.

5. The computerized method according to claim 4, wherein the user interface is a single-pane-of-glass user interface (SPoG UI).

6. The computerized method according to claim 4, wherein the master data governance (MDG) module generates an indication of one or more errors that occurred in a catalog conversion process associated with one or more converted catalogs.

7. A non-temporary computer-readable medium having multiple stored instructions, wherein when the multiple instructions are executed by a processor, the processor... The operation of receiving two or more diverse catalog files from the user through the user interface, The process involves using a catalog conversion module to convert the catalog file into a standardized format, continuously incorporating vendor reviews of categorization and attribute mapping to refine and improve the categorization and attribute mapping, incorporating that feedback into iterative learning, and predicting the categorization and attribute mapping for at least one catalog item in the catalog file. A real-time data mesh (RTDM) module, including an integration layer, a data layer, an AI module, a data engine layer, and an experience layer, is used to ensure data exchange and synchronization between system components by synchronizing the catalog files converted by the catalog conversion module with the data layer. Using the Master Data Governance (MDG) module, the catalog file converted by the Catalog Conversion module is subjected to at least one of the following operations: schema validation, detection of identical SKUs, and attribute consistency checks using the master data, thereby identifying one or more structural errors, classification errors, or pricing inconsistencies in the category classification and attribute mapping predictions made by the Catalog Conversion module. The operation facilitates the operation of the master data governance module by using the Global Data Repository (GDR) to store the catalog files verified by the master data governance module and by maintaining the data integrity of the stored catalog files, Using the search platform, the operation of indexing the catalog files stored in the global data repository is performed to facilitate real-time searching of SKU records for optimal product discovery and classification. Using a dynamic SKU creation module, when one or more non-transactional products are added to a customer's cart, the ERP system generates an SKU for the non-transactional product. A computer-readable medium that uses a global pricing engine (GPE) to determine real-time pricing for the catalog items, and that causes the pricing to be determined based on one or more of vendor price files, market trends, and pricing history data.

8. The computer-readable medium according to claim 7, wherein the user interface is a single-pane-of-glass user interface (SPoG UI).

9. The computer-readable medium according to claim 7, wherein the master data governance (MDG) module generates an indication of one or more errors that occurred in a catalog conversion process associated with one or more converted catalogs.

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